# Good Intentions, Risky Inventions: A Method for Assessing the Risks and
Benefits of AI in Mobile and Wearable Uses

Marios Constantinides  Nokia Bell LabsCambridgeUnited Kingdom
<EMAIL_ADDRESS>,  Edyta Bogucka  Nokia Bell
LabsCambridgeUnited Kingdom<EMAIL_ADDRESS>,  Sanja
Scepanovic  Nokia Bell LabsCambridgeUnited Kingdom sanja.scepanovic@nokia-
bell-labs.com and  Daniele Quercia  Nokia Bell LabsCambridgeUnited Kingdom
<EMAIL_ADDRESS>
###### Abstract.

Integrating Artificial Intelligence (AI) into mobile and wearables offers
numerous benefits at individual, societal, and environmental levels. Yet, it
also spotlights concerns over emerging risks. Traditional assessments of risks
and benefits have been sporadic, and often require costly expert analysis. We
developed a semi-automatic method that leverages Large Language Models (LLMs)
to identify AI uses in mobile and wearables, classify their risks based on the
EU AI Act, and determine their benefits that align with globally recognized
long-term sustainable development goals; a manual validation of our method by
two experts in mobile and wearable technologies, a legal and compliance
expert, and a cohort of nine individuals with legal backgrounds who were
recruited from Prolific, confirmed its accuracy to be over 85%. We uncovered
that specific applications of mobile computing hold significant potential in
improving well-being, safety, and social equality. However, these promising
uses are linked to risks involving sensitive data, vulnerable groups, and
automated decision-making. To avoid rejecting these risky yet impactful mobile
and wearable uses, we propose a risk assessment checklist for the Mobile HCI
community.

mobile, wearables, sustainable development goals, risk assessment, LLM, prompt
engineering

††copyright: acmlicensed††journal: PACMHCI††journalyear: 2024††journalvolume:
8††journalnumber: MHCI††article: 262††publicationmonth: 9††doi:
10.1145/3676507††ccs: Human-centered computing Ubiquitous and mobile
computing††ccs: Computing methodologies Artificial intelligence

Figure 1. Overview of our method. A three LLM-prompt pipeline for generating
mobile and wearable uses of AI (prompt #1), classifying each use’s risks
according to the EU AI Act (Comission, 2021) (prompt #2), and determining
whether each generated use is beneficial according to the UN’s Sustainability
Development Goals (United Nations: Department of Economic and Development,
2023) (prompt #3). Out of 138 generated uses, as many as 80 were considered
high risk according to the EU AI Act, primarily aligning with Sustainable
Development Goals 3 (good health and well-being), 10 (reduced inequalities),
and 16 (peace and justice). Our method was validated by two experts in mobile
and wearable technologies, a legal and compliance expert, and a cohort of nine
individuals with legal backgrounds who were recruited from Prolific,
confirming its accuracy to be over 85%.

Overview of our method.

††footnotetext: Project website: https://social-dynamics.net/risky-mobile

##  1\. Introduction

The integration of Artificial Intelligence (AI) into mobile and wearable
devices has unlocked new capabilities, from improving workplace productivity
to enhancing healthcare and education. Mobile assistants and wearables,
equipped with various sensors such as accelerometers and heart rate monitors,
have significantly increased workplace productivity (Mirjafari et al., 2019;
Aseniero et al., 2020; Choi et al., 2021; Constantinides et al., 2020). Smart
wearable devices provide low-cost, objective measures of physical activity,
mental health, and sleep quality assessments (Zhou et al., 2023; Park et al.,
2023; Constantinides et al., 2018), contributing significantly to scalable
behavior monitoring in large populations (Perez-Pozuelo et al., 2021). In
sports medicine, wearable technology can detect patterns in physiological
variables, assisting athletes in improving performance and preventing injuries
(Chidambaram et al., 2022). In education, smartwatches can be used to collect
real-time data during students’ learning activities, improving the analytics
and insights into learning processes (Ciolacu et al., 2019).

Despite the potential advantages, any technology comes with risks. Mobile and
wearable technology uses are no exception. Privacy concerns are particularly
prominent, as AI integration can lead to data leakage and unauthorized
surveillance (Haris et al., 2014; Constantinides and Quercia, 2022; Das Swain
et al., 2023). However, recent advances in federated learning (McMahan et al.,
2017) and differential privacy (Dwork, 2008) may enhance the data privacy of
AI-based mobile and wearable uses, potentially transforming high-risk uses
into low-risk ones. As these uses often involve automated decision-making, it
is crucial to provide explanations, especially in high-stakes domains. For
example, there is an increasing demand for explainable AI in healthcare to
ensure trustworthy AI-based decisions (Saraswat et al., 2022). Security issues
are also critical. In addition to well-known challenges such as the potential
to compromise sensitive data or use it to maliciously infer private
information (Crager and Maiti, 2017), wearable devices may also cause
physically harm to the wearer (Mills et al., 2016). For example, there have
been reports of skin irritation and allergic reactions caused by the materials
used (e.g., nickel in the metal components), or due to the accumulation of
moisture under the device, which can lead to skin irritation or even
infections (Kelly, 2014).

Traditional assessments of risks and benefits have been sporadic, and often
require costly expert analysis. In fact, risk identification requires
comprehensive domain expertise (Hassel and Özkiziltan, 2023). According to the
OECD, legal advisory costs required to ensure compliance with regulatory
systems of different countries is estimated to be nearly 780 billions dollars
per year worldwide (International Federation of Accountants, 2018; Moraes et
al., 2021). That is why traditional assessments of risks and benefits tend to
be fragmented, focusing on specific aspects of mobile and wearable use (e.g.,
efficiency in risk assessment for work-related activities (Yang et al.,
2018b), or adoption based on privacy risk versus perceived benefits (Li et
al., 2016)), and come with technical and ethical challenges. On the technical
side, mobile and wearables pose significant privacy risks (Cecchinato et al.,
2015), while, on the ethical side, these devices can potentially augment (or
replace) human intelligence in ways that might not always be desirable (Xu and
Ma, 2023). This dichotomy underscores the need for a more systematic approach
to evaluate the benefits and risks associated with these technologies. Issues
such as data privacy, the algorithms accuracy, and the potential for social
and individual opportunities (outlined, for example, by the United Nation’s
Sustainability Development Goals report (United Nations: Department of
Economic and Development, 2023)) must be considered to ensure that the
integration of AI truly benefits users without compromising their safety and
privacy (Puntoni et al., 2021). To address these challenges, we implemented a
semi-automatic way of conducting a risks and benefits assessment of AI for
mobile and wearable uses and, in so doing, we made two main contributions:

  1. (1)

We engineered and validated three Large Language Model (LLM) prompts capable
of (§4): _(a)_ generating a diverse range of realistic uses of AI for mobile
and wearable computing; _(b)_ classifying each generated use risks (whether it
is unacceptable, high risk, or low risk) according to the EU AI Act
(Comission, 2021); and _(c)_ determining the benefits associated with each
generated use. For classifying risks, we chose the EU AI Act because it is the
most advanced legal framework available at the time of writing, which
specifically adopts a risk-based approach at a use level (Comission, 2021).
This means that stakeholders involved in the development of an AI technology
are required to assess its risks for specific uses, rather than assessing the
technology itself for broad risks. For determining the benefits, we chose the
Sustainable Development Goals (SDGs). The SDGs are globally recognized and
allow for the evaluation of the AI’s long-term impacts related to five
priority areas that concern people, the planet, prosperity, peace, and
partnerships (United Nations: Department of Economic and Development, 2023).
To achieve these tasks, we used a set of novel prompt elements, including a
comprehensive list of _domains_ , five _risk concepts_ for describing uses,
and detailed manual _evaluations_ of prompts.

  2. (2)

Our method generated 138 uses, which we reviewed and thematically grouped them
in two categories (§5): uses promoting SDGs and being low risk; and uses
promoting SDGs yet being high risk. We found that specific applications of
mobile computing hold significant potential in improving well-being, safety,
and social equality. However, these promising uses are linked to risks
involving sensitive data, vulnerable groups, and automated decision-making.

In light of these results, we discuss challenges in conducting semi-automatic
assessments, and present a Risk Assessment Checklist for Mobile Computing uses
as a practical solution for balancing risks and benefits trade-offs in the use
of AI for mobile and wearable technologies (§6).

##  2\. Related Work and Background

Next, we surveyed previous literature that our work draws upon, and grouped it
into three main areas: _i)_ AI regulations and impact assessments (§2.1),
_ii)_ risk and benefits assessments of AI for mobile and wearable uses (§2.2),
and _iii)_ the use of LLMs to transition from manual to semi-automatic
assessment through prompt engineering (§2.3).

###  2.1. AI Regulations and Impact Assessments

The increasing deployment of AI has prompted calls for regulatory oversight
(Tahaei et al., 2023; Borenstein and Howard, 2021; Floridi et al., 2021). The
US Office of Science and Technology Policy has released a non-binding AI Bill
of Rights Blueprint, highlighting principles such as safety, non-
discrimination, data privacy, and AI transparency. Similarly, the European
Commission’s AI Act, which is legally binding, aims to balance innovation with
safeguarding societal values and rights. The Act classifies AI applications
based on risk levels, from low to unacceptable, and prohibits AI applications
that are harmful or manipulative (Comission, 2021, 2023). It emphasizes a
risk-based regulatory framework, requiring comprehensive evaluations for high
risk AI systems.

Typically, an impact assessment is conducted and a report is produced that
documents the potential impacts of AI. CredoAI’s report template includes
sections on formal system evaluations and compliance with laws, regulations,
and standards (Sherman and Eisenberg, 2023). Similarly, the report developed
by the National Institute of Standards and Technology (NIST) includes listings
of potential biases, evaluations of the impact’s magnitude and likelihood,
technical specifications, third-party technologies, and legal and compliance
issues (National Institute of Standards and Technology, 2023a). More recently,
a study has identified the five necessary components (i.e., domain, purpose,
capability, AI user, and AI subject) to conduct regulatory risk assessment
according to the EU AI Act (Golpayegani et al., 2023). The _domain_ specifies
the industry or sector, such as health. The _purpose_ explains the goal, such
as improving well-being. The _capability_ describes the technology behind it,
such as mood and stress level tracking. The _AI user_ is the one using the
system, such as mental health professionals. The _AI subject_ is the one
affected by the system, such as patients. Our study operationalizes these five
components in a semi-automatic way using LLMs.

###  2.2. Risk and Benefit Assessments of AI for Mobile and Wearable Uses

With the upcoming enforcement of the EU AI Act (Comission, 2021), AI-based
technologies will be subject to legal compliance. This raises particular
concerns for the use of AI in mobile and wearables, as the Act highlights
factors that could classify certain uses as high risk or even unacceptable.
For example, mobile and wearable devices, often placed near or worn on the
body, operate in the background unnoticed, and collect personal information
about individuals. This characteristic may categorize some uses as high risk
due to the sensitive nature of the data handling. However, recent advances in
federated learning and differential privacy can ensure data privacy and
security for such applications, potentially transforming high-risk uses into
low-risk ones. Federated learning, for example, allows AI models to be trained
across multiple decentralized devices holding local data samples, and never
exchanging them (McMahan et al., 2017). Additionally, differential privacy
introduces mathematical guarantees to ensure that the output of AI models does
not reveal sensitive information, and allows data analysis without
compromising individual privacy (Dwork, 2008). Similarly, homomorphic
encryption methods offer the potential for performing computations on
encrypted data, providing robust security assurances (Gentry, 2009).
Additionally, mobile and wearable uses are often non-generalizable due to
limited training datasets (e.g., skewed towards predominantly white cohorts
(Sjoding et al., 2020)), introducing biases and limiting their applicability
across diverse populations. Therefore, it is important to understand the risks
these uses entail. Assessing both risks and benefits allows for a well-
informed perspective, puts risks into context, and helps determine which risks
are worth taking or which mitigation strategies should be prioritized to
preserve the benefits.

The traditional approach to assess risks and benefits in mobile and wearable
uses has often been sporadic, required expert knowledge, and primarily
confined in research papers. Heidel et al. (2020) conducted a scoping review
to identify benefits and risks arising from the introduction of health apps
and wearables into the German statutory health care system. They noted that
health apps and wearables lead to improved treatment quality through enhanced
patient monitoring, disease management, personalized therapy, and better
health education. However, they also expressed concerns about data privacy and
security. The primary concern regarding privacy and human rights with
wearables lies in the quantity and type of data that individuals provide.
Zuboff, in “The Age of Surveillance Capitalism,” (Zuboff, 2023) wrote that
“every time we encounter a digital interface we make our experience available
to ‘datafication’, thus ‘rendering unto surveillance capitalism’ its
continuous tithe of raw-material supplies". Experts typically test mobile and
wearable apps for privacy and security vulnerabilities using methods such as
man-in-the-middle attacks, eavesdropping, and packet injection (Cusack et al.,
2017; Fuster et al., 2023) to prevent attacks and data misuse.

To sum up, risks and benefits assessments for AI in mobile and wearable uses
have been hitherto hindered by two primary limitations. First, these analyses
often focus on individual AI use cases or only a selection of few, potentially
overlooking the broader spectrum of uses. Second, assessments are
predominantly conducted manually by mobile and wearable experts or
researchers, identifying potential benefits and risks through scoping reviews.
Overcoming these limitations requires the development of new approaches that
automate or semi-automate part of the assessment process. Next, we turn to
LLMs, which present a promising ground for exploring this approach.

###  2.3. LLMs and Prompt Engineering

LLMs such as OpenAI’s ChatGPT (OpenAI, 2023), which are trained on a large
corpora of text using cutting-edge deep learning techniques (Wei et al.,
2023), have the capability to produce text that closely resembles human
writing in response to various prompts. These models are extremely efficient
for tasks such as answering queries and generating content, including text
annotation (Gilardi et al., 2023; Lee et al., 2022; Chung et al., 2022) and
powering chatbots that assist with mental health concerns (Chen et al., 2023;
Sharma et al., 2023). LLMs are known for providing insights that often exceed
general public knowledge (Gilardi et al., 2023), playing a significant role in
collaborative processes between humans and AI (Chung et al., 2022; Tolmeijer
et al., 2022; Wu et al., 2022; Lee et al., 2022), and in some instances, their
performance can match that of expert opinions (Ömer Aydın and Karaarslan,
2022; Dowling and Lucey, 2023; Byun et al., 2023). To achieve the desired
output from LLMs, a set of best practices for prompt engineering has emerged
(Saravia, 2022; Shieh, 2023). Typically, these practices can be grouped into
five areas.

The first area concerns the choice of the prompt’s elements. This includes the
system’s role, which is key to initiating interactions with the language model
as it primes the model to behave in a manner that aligns with the role’s
expectations (Azure, 2023). Whether the model is instructed to act as a mobile
developer, a creative assistant, or a project manager, this initial framing is
a critical step in guiding the model’s responses and ensuring they are
contextually relevant and appropriately styled. Additionally, the role can be
extended with additional cues to direct the model to the desired output (e.g.,
initial words for the sentence the model is expected to complete (Azure, 2023;
Shieh, 2023)).

The second area is about the prompt’s elements arrangement. For example, the
order in which elements are placed greatly affects the outputs due to a
phenomenon called recency bias. It is recommended to place the most important
elements of the prompt at the beginning and end (Liu et al., 2023).

The third area is about the instructions to the model. Successful instruction
requires clear and precise communication of tasks. It involves choosing the
right action verbs (e.g., describe, summarize, explain), specifying the
preferred format of the output (e.g., JSON or a single sentence),
incorporating knowledge that is required for the model to learn. Part of the
instructions are also different learning methods that can boost the model’s
performance. Beyond zero-shot learning, where models depend only on their pre-
existing knowledge, few-shot learning provides task-specific examples of
input-output pairs, enhancing performance on specialized tasks. Chain-of-
thought (CoT) reasoning, which prompts the model to provide step-by-step
answers, is especially beneficial for tasks that involve counting and
mathematical problems (Wei et al., 2022a; Brown et al., 2020; Wei et al.,
2022b).

The fourth area is about adjustment of model parameters. For example,
temperature modulates the balance between unpredictability and predictability
in the produced text. Higher temperature values encourage more varied
responses, while lower values result in outputs that are more deterministic.

The fifth area is about the evaluation of the output. LLMs may generate biased
or incorrect content, a phenomenon known as hallucinations (Dziri et al.,
2022). Therefore, for tasks that require certain levels of quality or
creativity, the model’s output should be evaluated. Evaluations may include
human reviewers to guarantee that the content adheres to quality benchmarks
and remains authentic (Eldan and Li, 2023; Dziri et al., 2022).

As highlighted, traditional risks and benefits assessments of AI for mobile
and wearable uses have been sporadic, fragmented, and resource-intensive. LLMs
incorporating these best practices have the potential to uncover benefits and
risks in an automated manner.

##  3\. Author Positionality Statement

Recognizing the importance of author positionality is essential for
transparently examining our perspectives on methodology and analysis (Olteanu
et al., 2023; Frluckaj et al., 2022). In this paper, we situate ourselves in a
Western country (United Kingdom) during the 21st century, writing as authors
primarily engaged in academic and industry research. Our team comprises two
males and two females from Southern, Eastern, and Central Europe with diverse
ethnic and religious backgrounds. Our combined expertise covers a range of
areas, including human-computer interaction (HCI), ubiquitous computing,
software engineering, artificial intelligence, natural language processing,
data visualization, and digital humanities.

It is also important to acknowledge the use of a large language model as part
of our methodology. As researchers from a primarily Western institution, we
understand the importance of broadening the perspectives on methodology
presented in this paper, and retain responsibility for the content and
interpretation of the findings. Consequently, our positionality may have
influenced the subjectivity inherent in selecting our methodology, designing
our study, and interpreting and analyzing our data.

##  4\. Methods

We developed three LLM prompts (Figure 1). The first prompt generated a wide
range of AI uses for mobile and wearables, the second classified each use
(whether it is unacceptable, high risk, or low risk) according to the EU AI
Act (Comission, 2021), and the third determined whether each use is helpful
for reaching Sustainability Development Goals (United Nations: Department of
Economic and Development, 2023). We chose the EU AI Act for classifying a
use’s risks because it is the most advanced risk-based legal framework for AI
systems, and the SDGs for determining a use’s benefits because it is globally
recognized and allows for evaluation of long-term impacts related to people,
the planet, prosperity, peace, and partnerships. Each prompt was manually
validated by two authors to verify the correctness of its outputs. We also
consulted a legal and compliance expert to further validate the prompt’s
outputs as well as conducted a crowdsourcing study with a cohort of nine
individuals with legal backgrounds who were recruited from Prolific,
confirming the accuracy of our approach to be over 85%. As our LLM model, we
chose OpenAI’s GPT-4††https://cdn.openai.com/papers/gpt-4.pdf due to its best
performance across benchmarks,††https://lmsys.org/blog/2023-06-22-leaderboard/
and its demonstrated capability in interpreting legal documents (Zheng et al.,
2023). To allow for reproducibility, in line with open-source language models,
we made our prompts and code publicly available.††Available at the project’s
page: https://social-dynamics.net/risky-mobile

###  4.1. Prompt #1: Generating Uses

This prompt generates a list of AI uses for mobile and wearable technologies.
It achieves this by introducing two innovative prompt elements—a cue for
different _domains_ where these technologies could be applied, and a cue for
five _components_ that guide the specific description of each use and enable
risk assessment as per the EU AI Act.

Prompt #1 combines three elements: _system role_ , _instructions_ , and
_output format_.

First, the _system role_ is a _Senior Mobile and Wearable Systems Specialist,
with experience in sensing technologies and their applications in multiple
domains;_ this choice guides the model to generate content aligned with
advanced expertise required of this position (Giray, 2023). We selected this
role because it brings a comprehensive understanding of both the technical
aspects and practical deployment of wearable technologies, ensuring our study
addresses real-world applicability alongside theoretical advancements.
Compared to other potential roles (e.g., a Health Informatics Specialist,
Consumer Electronics Product Manager, or IoT Architect), a more broader
expertise is particularly suited to guide the development and deployment of
mobile and wearable technologies. Additionally, we included a cue to further
contextualize the role. This cue is a list of mobile and wearable sensors
(e.g., accelerometers, gyroscopes, photoplethysmography, cameras, and global
positioning systems) along with their possible placement (e.g., placed in
various objects or parts of the body, such as the torso, wrist, and pocket)
and their technical capabilities (e.g., monitoring human behavior, inferring
physical, mental, emotional, and social status).

Second, the _instructions_ include: (1) the definitions of five components
(Golpayegani et al., 2023) that are required for risk assessment as per the EU
AI Act (Comission, 2021). The five components are: the _domain_ , that
specifies the industry or sector (e.g., health); the _purpose_ , that explains
the goal (e.g., monitoring fitness); the _capability_ , that describes the
technology behind the use (e.g., mood and stress level tracking); the _AI
user_ , who operates the system (e.g., doctors); and the _AI subject_ , who is
affected by the system (e.g., patients); (2) a list of application 46 domains
(e.g., health, family, law enforcement), systematically developed based on the
EU AI Act by selecting eight high risk domains listed in Annex III and 32
additional domains inferred from the Act’s text and amendments (Comission,
2023). This list guides the LLM to generate specialized and nuanced uses
across individual, interpersonal, institutional, community, and public policy
contexts; (3) the task of generating three AI uses for each of the 46
application domains.

Third, the _output format_ specifies how the LLM’s output should be displayed
in the five-component format (e.g., for monitoring fitness using wearables
[Domain, Purpose, Capability, AI user, AI subject] becomes [“Health”,
“Monitoring fitness”, “Mood and stress level tracking”, “Doctors”,
“Patients”]).

Manual validation. After generating the uses, two authors with experience in
mobile and wearable computing reviewed each one of them. The authors
independently assessed each generated use to determine whether it is correct
and whether it is existing or upcoming. They considered all 138 generated uses
to be correct, with 128 (93%) already existing (practically viable and in
use), 9 (6%) being upcoming (practically viable but still in early prototype
stage), and 1 being unlikely (practically viable but only discussed as
possibilities in research papers).

###  4.2. Prompt #2: Classifying Use Risks

This prompt classifies the risks of each generated use according to the EU AI
Act (Comission, 2021). It combines four elements: _system role_ , _input_ ,
_instructions_ , and _output format_. First, the _system role_ is a _Senior AI
Technology Expert, specializing in compliance with the EU AI Act_. Second, the
input includes articles from the EU AI Act and its Annex III regarding high
risk AI systems. Third, the instructions use a Chain-of-Thought approach
(i.e., dividing the task into a series of smaller, intermediate reasoning
steps that lead to the final output (Wei et al., 2022b)). This approach
involves three steps: (1) drafting a concise description of the system’s use
through the five component-format; (2) producing a risk classification as per
the EU AI Act (whether the system’s use is unacceptable, high risk, or low
risk); and (3) providing justification for the classification. Fourth, the
output format of risk assessment as per the EU AI Act contains three fields:
“[Use Risk Classification], [Classification Justification], [Relevant EU AI
Act Article].”

Manual validation. Two authors familiarized themselves with the EU AI Act
articles, and manually classified each of the 138 generated uses as either
unacceptable, high risk, or low risk. The validation criteria aligned with the
specific requirements and definitions outlined in the EU AI Act concerning
risk levels associated with AI uses. We defined the following criteria:
potential impact on privacy and data protection, and the likelihood of adverse
outcomes affecting individuals or groups. Additionally, we considered the
operational contexts in which a mobile or wearable use is deployed such as the
type of data collected and the functionality of the device. 80 uses (58%) were
identified as high risk, 57 (41.3%) as low risk, and 1 (0.7%) use as
unacceptable risk. The agreement between the two authors and the LLM’s
classification was 85%. In the rest 15% of cases in which LLM was wrong, the
disagreements were about uses related to vulnerable groups, personalization,
government and democracy, and critical infrastructure (e.g., energy), all of
which the LLM classified as low risk instead of high risk. The only use
categorized as unacceptable risk (where both the two authors and the LLM
agreed) was related to enhanced surveillance capabilities using cameras. These
disagreements were resolved in two steps. First, the two authors held an
initial review meeting in which they discussed their assessments against those
done by the LLM, and consulted the rest of the research team. Second, we
consulted a compliance expert specialized with the EU AI Act to inspect the
15% of the uses that were wrongly classified by the LLM. The expert is an
academic affiliated with TU Delft in Netherlands and works at the intersection
of technology, policy and management. The expert manually classified each
use’s risk (i.e., unacceptable, high risk, or low risk), confirming that
indeed these uses were wrongly classified by the LLM. Upon the consultations
with the team and the expert, we reached the final decision.

###  4.3. Prompt #3: Determining Use Benefits

This prompt determines whether each generated use is helpful for reaching
Sustainability Development Goals (United Nations: Department of Economic and
Development, 2023). It combines four elements: _role_ , _input_ ,
_instructions_ , and _output format_. First, the role is a “Senior Specialist
in the field of Mobile and Wearable Technologies with a dedicated focus on
understanding, promoting, and implementing the SDGs”. Second, the input
includes the 17 definitions of the SDGs, covering a broad range of potential
areas for beneficial AI applications across the economy, society, and the
environment. Third, the instructions (using Chain-of-Thought as in the
previous prompt) involve: (1) summarizing the system’s use based on the five-
component description; (2) listing the benefits, reflecting how the use
promotes each SDG. Fourth, the output format contains two fields: “[SDG Goal],
[Reasoning for Promoting the Goal]”.

Manual validation. Two authors familiarized themselves with the 17 definitions
and descriptions of the UN Sustainable Development Goals, and manually
reviewed the feasibility of benefits associated with each goals (United
Nations: Department of Economic and Development, 2023). To ensure a robust
manual validation, the authors defined the following criteria: _a)_ the
applicability of the use to an SDG goal is evident; and _b)_ the feasibility
of benefits is evaluated based on current technological capabilities. The
agreement between the two authors and the LLM’s output was 61%. Disagreements
were mainly related to goals 5 (gender equality), 16 (peace, justice and
strong institutions) and 17 (partnerships for the goals), all of which were
manually corrected. Disagreements were resolved in a similar way to
classifying uses risks. First, the two authors discussed disagreements among
them, and then consulted the rest of the research team to reach a final
decision.

###  4.4. Crowdsourcing Study

Setup and Procedure We developed a web-based survey and administered it on
Prolific††https://www.prolific.com/, which included the LLM classifications
for each use (Figure 2). The survey consisted of six pages. The first page
outlined the study’s description and the two tasks that the crowdworkers had
to execute. Initially, they were asked to read the definitions of risky’ and
beneficial’ uses, and then they were presented with assessment cards for 46
uses to assess. The second and third pages included the definitions of risky
and beneficial uses according to the EU AI Act and the SDGs, respectively. The
fourth and fifth pages presented 46 uses (23 each in page), where trap
questions were interspersed to ensure data quality. We added two types of trap
questions: the first type included two attention checks: _When asked for your
favorite color, you must select ‘Green’_ ; and _When asked for your favorite
city, you must select ‘Rome’._ Participants had to correctly respond to these
checks after completing each task. The second type included an unacceptable
use (i.e., a social scoring system based on facial recognition in public
spaces) according to the EU AI Act, which we deliberately classified as low
risk. Participants who failed either of the attention checks or to correct the
classification of this use were disregarded and not paid. Additionally, we
disabled pasting from external sources and editing previous responses to
ensure original and thoughtful answers. The final page included a thank-you
note and redirected participants on Prolific. To ensure reproducibility and
assist researchers who aim to use our methodology, we have made the code for
our survey publicly available.††Available at the project’s page:
https://social-dynamics.net/risky-mobile

Participants. To recruit individuals with a background in legal and
compliance, we relied on Prolific’s screening criteria. We searched for
participants likely involved in revising AI systems as part of their legal
roles, using AI at least 1-6 times a week. In total, we recruited 9
participants with “legal” as the “function in organization”, suggesting that
these individuals had prior legal expertise (Table 1). Six self-identified as
males and three as females, with their ages ranging between 21 and 49 years
old ($\mu=37$ and $\sigma=8.73$). Their current countries of residence were
within the European Union, including participants from Italy, Poland, Germany,
the Netherlands, Portugal, and Slovenia. On average, participants took 40
minutes to 1 hour to complete a batch of 46 uses, and were compensated €9.29.

Figure 2. Crowdsourcing study survey. Participants were given instructions about the study (1) and were provided the definitions of risk classifications as per the EU AI Act (2) and the definitions of the 17 Sustainable Development Goals (3). Then participants were presented with 46 mobile HCI uses (4,5) and asked to assess how probable is the use (Q1), whether they agree with the LLM-generated risk classification (Q2) and its justification (Q3), explain their reasoning about the classification and justification (Q4), and select the SDGs that they believe the use supports. After annotating all uses, they were redirected to the Prolific confirmation page (6). Table 1. Demographics of participants in the crowdsourcing study. Batch |  ID |  Gender |  Age |  Country |  Weekly AI use
---|---|---|---|---|---
|  P1 |  Male |  33 |  Italy |  Every day
B1 |  P2 |  Male |  41 |  Poland |  2-6 times a week
|  P3 |  Male |  38 |  Germany |  2-6 times a week
|  P4 |  Male |  40 |  Germany |  2-6 times a week
B2 |  P5 |  Female |  47 |  Italy |  2-6 times a week
|  P6 |  Male |  25 |  Poland |  Multiple times every day
|  P7 |  Female |  49 |  Portugal |  Every day
B3 |  P8 |  Female |  36 |  Slovenia |  Every day
|  P9 |  Male |  21 |  Germany |  Multiple times every day

Analysis and Results. Each use was annotated by three crowdworkers, and each
of them annotated a total of 46 uses (i.e., all 138 uses were divided into
three batches).

When they classified the three risk levels based on the EU AI Act
classifications, there was a full agreement in 48% of uses. Full agreement was
about uses that have clear benefits and utility (e.g., tracking health
status); are widely-adopted (e.g., facilitating mobile payments); or are in
already regulated domains (e.g., enhancing food safety). Conversely,
disagreements were about uses that present privacy, security, and ethical
concerns (e.g., tracking vulnerable groups like children and the elderly); or
are deemed emerging (e.g., mobile VR for educational content). We then used
majority voting to establish the final classifications by the crowdworkers and
compared them against the LLM classification. The agreement was 92.75%. By
manually inspecting the disagreements, crowdworkers corrected the LLM
classification and cited similar reasons to those reported during the two
authors’ manual validation (e.g., cases involving uses about democracy and
personalization).

When the crowdworkers then determined the 17 SDGs applicable to the uses at
hand (one use could have multiple SDGs assigned), there was a full agreement
in 11% of uses (i.e., crowdworkers selected the exact same SDG(s) for each
use). Full agreement was about uses that had a clear positive impact on daily
life (e.g., monitoring health status with wearables); aligned with broader
societal goals (e.g., improving election transparency); or improved safety and
security (e.g., enhancing surveillance capabilities for public safety).
Conversely, disagreements were about uses with varied perceptions of
usefulness (e.g., tracking emotions for well-being); or had privacy, security,
and ethical concerns (e.g., involving facial recognition). For each use, we
then took the union of the three crowdworkers’ annotations to establish the
final classification and compared this list against the LLM classification.
The agreement was 37.89%. Compared to the two authors’ validation (61%
agreement with the LLM classification), crowdworkers had lower agreement. This
greater discrepancy could be attributed to the difficulty in determining the
applicability of SDGs, which may require expertise beyond the legal domain.
Additionally, it requires complex cross-referencing of the use with 17
different goals and their numerous targets.

##  5\. Results

Having the set of 138 generated uses and their associated risks and benefits
(detailed in Table LABEL:tab:all_uses in the Appendix; the notation #
indicates the use’s ID), we report the results in two steps. First, for each
SDG, we computed the proportion of beneficial uses categorized as low risk
(§5.1) and high risk (§5.2), and then discuss example uses from the goals with
the highest proportions of each risk category. Second, we conducted a thematic
analysis to understand the reasons why a use is risky or not, and why it is
beneficial (§5.3). Note that among the 138 uses, only one was classified as
unacceptable according to the EU AI Act. This use involves _enhancing
surveillance capabilities using mobile cameras in public spaces_ (use #64). In
our analysis, we did not focus on unacceptable uses because they are banned.
Instead, high-risk AI uses, which are allowed to be deployed, must be subject
to an impact assessment for compliance purposes. Therefore, our analysis
focuses on distinguishing between high-risk and low-risk uses.

###  5.1. Uses Promoting SDGs and Being Low Risk

Uses that pose low risk primarily promote goals related to environmental
protection, such as life below the water (goal 14), life on land (goal 15),
and clean water and sanitation (goal 6) (navy bars in Figure 3). These uses
are classified as low risk by the EU AI Act. Next, we discuss examples of
these uses.

:

_Monitoring crop health and growth, use #49._ IoT sensors are used to
continuously monitor environmental parameters such as temperature, humidity,
and soil moisture, alerting farmers via mobile apps if conditions are
unsuitable for their crops (Tyagi et al., 2020). Such uses promote sustainable
agricultural practices and environmental conservation (goal 15). Similar uses
can involve in vivo plant sensors (e.g., organic electrochemical transistor-
based biosensors (Vurro et al., 2023)) that detect nutrient deficiencies or
drought stress and allow for the precise application of agrichemicals,
fertilizers, and water, thereby maximizing yields (Roper et al., 2021).

:

_Tracking livestock health and location, use #50._ GPS collars have been
effectively used to track livestock, including their location, movement
patterns, and grazing behavior (e.g., by creating “virtual geofences” which
alert farmers if an animal strays from a designated area (Ilyas and Ahmad,
2020)); uses that enables precise livestock management (goal 15). These
collars can also be equipped with more specialized sensors to monitor the
behavioral and physiological parameters of livestock, allowing farmers to
assess the animals’ health and welfare over time. Examples include pedometers
and microelectromechanical (MEMS) activity sensors, which are easily attached
to animals (Helwatkar et al., 2014). Additionally, tracking livestock
movements and interactions can help in understanding and managing disease
outbreaks, such as those caused by the influenza virus. (Neethirajan, 2017).

:

_Optimizing irrigation systems, use #51._ IoT sensors have been used to
optimize water usage (goal 6) and predict harvest times (goal 15). Mohamed et
al. (2021) demonstrated a smart irrigation system equipped with sensors for
monitoring water levels, irrigation efficiency, and climate conditions.
Wearable soil moisture sensors, strategically placed throughout a field, can
now transmit data to a central system, accessible via mobile devices. Farmers
can adjust irrigation schedules directly from their phones, optimizing water
usage based on real-time soil moisture data. This approach not only preserves
(and saves) water but also ensures that crops receive the appropriate amount
of water at the right time, which is essential for crop health and yield
(Obaideen et al., 2022).

:

_Monitoring environmental conditions, use #112._ IoT sensors are instrumental
in monitoring a wide range of environmental parameters (e.g., heat, sound,
pressure, temperature, air quality, water quality), promoting sustainable
management of terrestial ecosystems (goal 15). Integrating these sensors into
smart environmental monitoring systems allows for large-scale environmental
assessments (Andrachuk et al., 2019). Battery-free, wireless cameras and
sensors can now be deployed in underwater environments to monitor water
quality, marine life, and underwater ecosystems (Afzal et al., 2022) (goal
14). These sensors are not only limited to monitoring air and water quality
but also extend to waste management (Laha et al., 2022). For example, in
domestic environments, they can effectively monitor conditions such as
temperature, humidity, and gas levels, with the data being easily accessible
through mobile apps or web pages (Chaudhari et al., 2018).

:

_Tracking wildlife movements, use #113._ GPS collars are used to study animal
behavior, and understand their habitat preferences and migration behaviors
(Allan et al., 2013) (goal 15). These technologies also enable real-time
monitoring of wildlife movements, including proximity, geofencing, movement
rate, and immobility (Wall et al., 2014) and can enable modeling epidemics in
wildlife hosts, such as outbreaks of dolphin morbillivirus (Morris et al.,
2015). Similar uses can involve multi-sensory wearable devices that employ
neural networks for classifying animal behavior in national parks (Rios-
Navarro et al., 2016) or for predicting animal personality (Meegahapola et
al., 2023). Wireless sensor networks track small turtles, monitoring their
micro-climate and hibernation periods (Joshi et al., 2008), while GPS-powered
systems can effectively track wild animals straying from sanctuaries, aiding
in poaching prevention (Gor et al., 2017).

Figure 3. Percentages of uses categorized as low risk and high risk for each
goal. The total number of uses per goal is in parentheses below the goal’s
name.

###  5.2. Uses Promoting SDGs yet Being High Risk

Uses that promote certain goals but pose high risks primarily relate to peace,
justice, and strong institution (goal 16), good health and well-being (goal
3), and reduced inequalities (goal 10) (gold bars in Figure 3). These uses
entail high risks, either due to the use of risky technology (e.g., cameras
for facial recognition, or motion sensors paired with physiological sensors),
or because they operate in high risk domains such as migration management,
military and defense, or healthcare involving individuals from vulnerable
groups. These uses include:

:

_Enhancing security through facial recognition, use #1._ High-performance
cameras in smartphones have been typically used for various authentication
purposes such as phone unlocking, banking, and access control, by analyzing
face images (Corsetti et al., 2019; Buriro et al., 2016). Such uses ensure
safety and security by preventing unauthorized access (goal 16). However, as
per EU AI Act’s Article 6 “biometric identification and categorisation of
natural persons” is under the high risk category. However, the very same
technology (i.e., cameras) may well be used for crowd surveillance; a use that
has unacceptable risks.

:

_Identifying individuals in criminal investigations, use #3._ Smartphones and
wearable watches can provide crucial information in criminal investigations
(e.g., documenting an individual’s activities), which can be used to determine
the veracity of witness testimony (Dorai et al., 2020). For example, a
smartphone synced with a wearable watch can offer evidence related to the
execution of a violent act, aiding in pinpointing the time and nature of the
incident (McNary and Hunter, 2018) and ensuring safety and security (goal 16).
Suspicious pattern detection techniques from mobile device data can also help
identify individuals involved in activities such as cyberbullying or low-level
drug dealing (Barmpatsalou et al., 2018). According to EU AI Act’s Article 6,
the amendment 711 classifies such a use as risky because it can used by “law
enforcement authorities for making individual risk assessments of natural
persons in order to assess the risk of a natural person for offending or
reoffending or the risk for potential victims of criminal offences”.

:

_Monitoring children’s location for safety, use #4._ Multi-sensor wearable
such as wristbands and IoT-enabled devices have been designed to track
children’s location, body temperature, humidity, and heartbeat, with the
capability to alert parents through SMS text messages in case of emergency
(Chowdhury et al., 2019; Sunehra et al., 2020). Such uses strive for improving
children’s well-being and safety (goal 3). However, as per the EU AI Act’s
article 69 and amendment 634, the use is risky because it “may affect
vulnerable persons or groups of persons, including children, the elderly,
migrants and persons with disabilities”.

:

_Monitoring elderly health and activity, use #5._ Wearable devices, such as
wristbands equipped with sensors like accelerometers, gyroscopes, and heart
rate monitors, are used for activity detection and monitoring in elderly care
and rehabilitation (Nweke et al., 2019), improving their well-being and safety
(goal 3). More broadly, IoT-based smart healthcare systems provide real-time
monitoring of the elderly’s location, activity patterns, and health status,
enabling early detection of health risks and improved response to emergencies
(Lin et al., 2022). As this use may affect vulnerable groups is classified as
risky under the EU AI Act’s article 69 and amendment 634. However, the same
on-body sensors could be used for surveillance, a concept Yuval Noah Harari
termed “under-the-skin surveillance” (Harari, 2021), introducing unacceptable
risks. This type of surveillance goes beyond traditional monitoring by
collecting intimate physiological data directly from a person’s body,
potentially invading privacy and autonomy on a deeply personal level.

:

_Enhancing mental health treatment, use #12._ Wearable sensors for
electrodermal activity and photoplethysmography provide real-time
physiological data crucial for mental health monitoring and treatment
(Fletcher et al., 2010), ensuring good (mental) health and well-being to
individuals (goal 3). A comprehensive study highlighted 46 systems focusing on
continuous monitoring, diagnosis, and care of various mental disorders through
mobile and wearable technologies (Bardram and Matic, 2020). Music streaming
services, when repurposed as therapies for affective disorders, exemplify the
potential of these technologies in mental health treatment (Schriewer and
Bulaj, 2016). Additionally, smartphones and wearable devices support mental
health research by facilitating the collection of novel naturalistic and
longitudinal data relevant to psychiatry (Torous et al., 2017). However,
similarly to uses affecting children or elderly, this use is again classified
as risky the EU AI Act’s article 69 and amendment 634 as it may affect
vulnerable groups who suffer from mental health problems.

:

_Preventing unauthorized access, use #43._ Wearable devices are used in access
control systems to protect information and define restrictions on information
handling (Diez et al., 2019) in military facilities, prevent unauthorized data
leakage, and safeguarding sensitive information (Yang et al., 2018a) (goal
16). These devices can also be used in user authentication, improving the
security of mobile payment systems and other applications where sensitive data
is involved (Wong and Kim, 2016). However, As per the EU AI Act’s Article 6
and amendment 711, the use is risky because it can be used for “biometric
identification”.

:

_Predicting infrastructure failures, use #62._ Wearable sensors have been
applied to predict health-related issues, such as COVID-19 incidence, by
tracking the spread of the virus, demonstrating their potential in predictive
health infrastructure (Seshadri et al., 2020). Analyzing mobility patterns in
IoT networks (e.g., georeferenced traces that citizens leave when they
navigate a city) are used to predict infrastructure failures and optimize road
traffic (Fazio et al., 2021) such as road traffic, ensuring inclusive access
to society (goal 16). However, the EU AI Act’s amendments 713 and 714 classify
the use as risky because it is intended to be used as “safety component in the
management and operation of road traffic and the supply of water, gas, heating
and electricity”.

:

_Enhancing surveillance capabilities, use #64._ Mobile cameras,
accelerometers, and gyroscopes are typically employed for continuous
monitoring and activity detection in surveillance applications (Cornacchia et
al., 2016), ensuring safety by maintaining public order and general welfare in
a democratic society. The concept of “sousveillance,” which refers to
surveillance from below as opposed to from above, has been facilitated by
wearable devices, generating new types of information in social surveillance
situations (Mann et al., 2003). Mobile sensors, like those in Android
smartphones, are used in real-time surveillance systems for tasks such as
human face tracking (Saraceni et al., 2012), even in public spaces. High-tech
gear like textile-based wearable microphones, miniature cameras, and wireless
personal displays can also enhance surveillance capabilities while appearing
natural to the user (Venkateswarlu et al., 2006). However, as per the EU AI
Act’s article 5.1, the “use of real-time remote biometric identification
systems in publicly accessible spaces, unless and in as far as such use is
strictly necessary”.

###  5.3. Understanding the Reasons Why a Use Is Low Risk, High Risk, and
Beneficial

Two authors conducted an inductive thematic analysis (bottom-up) to understand
the reasons why a use is risky or not, and why it is beneficial (i.e., as per
Prompt 2 and 3 outputs), following established coding methodologies (Saldaña,
2015; Miles and Huberman, 1994; McDonald et al., 2019). The authors used
sticky notes on the Miro platform (Miro, 2022) to capture the reasons and
inductively construct the themes. They held four meetings, totaling 8 hours,
to discuss the emerging themes that arose during the analysis process. Next,
we discuss the resulting themes and grouped them into three categories, that
is, reasons for uses being: _a)_ low risk; _b)_ high risk; and _c)_
beneficial.

Low Risk: Low risk uses were about the _environment and sustainability_ , and
_logistics_. The reasons why these uses are low risk are because they handle
data about non-human subjects and the environment, and operate in domains that
are not considered high risk. For environment and sustainability, mobile and
wearables enable environmental monitoring (e.g., air and water quality) that
allow for timely interventions to mitigate pollution; and monitor energy
consumption and provide feedback to users for encouraging energy-saving
behaviors and contributing to carbon footprint reduction. For logistics,
wearables devices equipped with RFID or GPS technology enable real-time
tracking of goods that can significantly reducing the time needed for
inventory management and supply chain operations; sensor-equipped wearables
(e.g., soil moisture sensors) provide precise information on crop needs to
help minimize the use of water and fertilizers.

High Risk: High risk uses were about the _sensitive data handling_ ,
_vulnerable user groups_ , and _automated decision making_. The reasons why
these uses are high risk are because they generally collect sensitive data,
even about vulnerable individuals at time or engage in automatic decision
making aimed at behavioural change. For sensitive data handling, the uses
involved collecting data with different levels of sensitivity: biometric data
such as facial images or footprints, voice prints or gait data, and other
personal data which can reveal information about physical, physiological or
behavioural traits of people such as fitness data or financial records. For
vulnerable user groups, the uses were affecting vulnerable persons or groups
of persons, including children, the elderly, migrants, and persons with
disabilities. For automated decision making, the uses were designed in a way
to influence habits (e.g., recommender systems), leading to potential negative
impacts on mental health and social interactions.

Beneficial: Independent to whether uses are risky or not, they were beneficial
mainly because they _foster participation and collaboration_ in society,
_improve well-being_ , and _improve safety and security_. For fostering
participation and collaboration in society, mobile and wearables enable
seamless communication and connectivity, breaking down geographical and
temporal barriers; allow users through apps to share their experiences,
achievements, and challenges, fostering a sense of community and mutual
support. For improving well-being, mobile and wearables allow for physical
activity and sleep patterns monitoring; used to detect early signs of health
issues and provide recommendations for stress management (e.g., meditation and
psychological support). For improving safety and security, emergency response
apps allow users to quickly alert emergency services; GPS tracking features on
mobile and wearables are beneficial for monitoring the safety of children, the
elderly, or individual in high risk environments.

##  6\. Discussion

Our method offers a semi-automatic way to assess the risks and benefits of AI
uses in mobile and wearables using three LLM prompts. We found that low risk
uses primarily benefit the environment and logistics. Conversely, high risk
uses, while promising improved well-being, safety, and equality, come with
significant concerns over sensitive data, vulnerable groups, and automated
decision-making. These uses often collect biometric data from cameras or
operate in high risk domains such as healthcare, particularly when involving
vulnerable groups. Next, we discuss challenges in conducting such an
assessment highlighting our lessons learned, and present practical solutions
(e.g., a checklist) for balancing the trade-offs between risks and benefits in
mobile and wearable technologies.

###  6.1. Challenges in Conducting Risks and Benefits Assessment Using LLMs

The use of LLMs to assess the risks and benefits of AI (in our case, on mobile
and wearable technologies) presented unique challenges, which are likely to be
applicable to other domains as well. Although LLMs may be able to provide a
comprehensive analysis across various use cases, risks, and benefits, their
outcomes should be treated as preliminary steps rather than definitive
conclusions. We specifically highlight three areas that require careful
attention.

First, a key challenge is ensuring the accuracy and relevance of the LLM’
output, as these models heavily rely on the quality and scope of their
training datasets. We overcome this challenge by employing a number of best
prompt engineering practices (Saravia, 2022; Shieh, 2023) such as specifying
the model’s role (e.g., senior mobile and wearable specialist), detailing its
task(s), providing additional knowledge (e.g., excerpts of the EU AI Act), and
defining the expected output format.

Second, the complexity and often opaque nature of LLMs make their output’s
interpretation challenging. To ensure that the language model produced
realistic uses, and, in turn, classified each use’s risks and determined its
benefits, two authors manually inspected the output in every step of the
process. A human-in-the-loop approach helps to alleviate model’s
hallucinations. Additionally, by employing a Chain-of-Though approach (Wei et
al., 2022b), we ensured that the model explained its reasoning to arrive at
the final output.

Finally, LLMs might not fully capture all ethical and societal dimensions
linked to mobile and wearables. More broadly, identifying risks associated
with AI systems requires deep organizational, technical, and regulatory
expertise (Buçinca et al., 2023), which can be achieved by integrating
experts’ knowledge in the process. The use of AI for evaluating AI systems
(and their use) introduces a number of ethical considerations that require
careful examination. One prominent concern is the recursive nature of such
assessments, wherein AI systems are employed to evaluate outputs generated by
similar AI systems, potentially leading to feedback loops that amplify biases
or errors without sufficient human oversight (Hagendorff, 2020). Another
concern is about transparency. Documenting the decision-making processes, the
criteria used for evaluations, and the limitations of the AI tools employed is
essential for allowing independent verification and fostering trust
(Mittelstadt et al., 2016). For example, to allow for reproducibility, we made
our code publicly available.

Despite the advancements in LLMs capabilities, there are inherent limitations
in their ability to make judgments against complex ethical standards. Current
LLMs may struggle to comprehend and apply nuanced ethical principles, leading
to potential gaps and inconsistencies in such assessments (Jobin et al.,
2019). Addressing these limitations requires ongoing research aimed at
enhancing LLMs ethical reasoning capabilities and ensuring alignment with
universally accepted ethical norms and standards. Additionally, we foresee
that semi-automated ways for conducting risk and benefits assessments should
be complemented with human-in-the-loop validations and/or non-AI approaches
(e.g., crowdsourcing studies), which we discuss next.

###  6.2. Challenges in Conducting AI-Based Risk and Benefit Assessments
Versus Human-Based Assessments

Next, we turn into discussing challenges for conducting risks and benefits
assessments using AI-based approaches (e.g., LLMs) versus human-based ones
(e.g., crowdsourcing). While AI-based assessments offer speed, scalability,
and consistency, our findings reveal several challenges that call for a
combined approach incorporating both methodologies.

The first challenge is about consistency and contextual judgment. Our AI-based
assessment using LLMs demonstrated, to a great extent, high agreement rate
with human judgments when assessing the risk classification level of mobile
and wearable uses. However, this consistency may overlook nuanced
interpretations that human experts can provide. Human assessors, especially
those with specialized knowledge, can offer contextual judgments that AI might
miss, as evidenced by the adjustments made by crowdworkers based on deeper
understandings of democracy and personalization issues. In other words, this
illustrates the limitations of AI in areas requiring ethical consideration and
complex contextual analysis, suggesting that while AI can guide assessments,
it should not replace human judgment.

The second challenge is about the depth of understanding. This was
particularly evident in the lower agreement rates on SDGs, where human
expertise was crucial for comprehensive evaluation. This difference suggests
that AI should operate as an exploratory tool, with final decisions deferred
to human experts who can assess the broader ethical and societal implications
of a use.

The third challenge is about reliability. The human-based approach introduced
diverse judgment perspectives, but at the same time demonstrated potential
inconsistencies. This variability, while introducing diverse viewpoints, also
necessitates rigorous control measures to ensure reliability (e.g., using trap
questions and attention checks). One possibility would be to use AI-based
assessments as a baseline for consistency and comparison with human-based
ones.

Given these challenges, we believe that a promising approach should combine
both AI-based assessment and human-based ones. Such a hybrid approach would
use AI for initial assessments due to its efficiency and scalability, and
human experts for providing the final judgments. To aid researchers interested
in adopting a similar approach in different contexts, we recommend following
these steps: _(1)_ describe a use case using the five-component format
suggested by Golpayegani et al. (2023) (i.e., Domain, Purpose, Capability, AI
User, AI Subject) as required for risk assessment under the EU AI Act
(Comission, 2021); _(2)_ incorporate expert human validation with clear
intervention protocols throughout the process; and _(3)_ implement continuous
feedback loops between the AI-based assessments and the human-based ones.

###  6.3. Balancing the Trade-Offs Between Risks and Benefits of AI in Mobile
and Wearable Uses

The use of mobile and wearables in sensitive or high-stakes domains
underscores the need to balance risks and benefits. While these technologies
contribute significantly to Sustainable Development Goals such as good health
and well-being, reduced inequalities, and peace and justice, they also pose
inherent risks due to their sensitive nature. Challenges include privacy
concerns, potential misuse for surveillance, and the risk of exacerbating
existing inequalities or societal issues. For example, sensitive personal data
(e.g., health information) could be misused if not adequately protected,
leading to unauthorized access, surveillance, or infringement on individual
freedoms and rights. Additionally, it is hard to obtain large representative
samples across diverse populations when training models for mobile and
wearable uses, making such uses more susceptible to biases (Sjoding et al.,
2020).

Figure 4. Risk Assessment Checklist for Mobile Computing. It helps to
systematically consider a mobile or wearable system’s: (A) use, (B) data, (C)
risks, (D) mitigations and (D) compliance with legal and regulatory standards.

To balance risks and benefits requires active research, policy updates, and
open dialogue as to what is considered as an acceptable technology use
(Constantinides and Quercia, 2022). For example, it is essential to enhance
privacy protections beyond those outlined in the EU AI Act as mobile and
wearables capabilities continue to evolve. This can be achieved not only by
understanding the direct impact of these technologies (e.g., producing impact
assessment cards similar to model cards (Mitchell et al., 2019)) but also by
considering their broader societal implications. It is of prime importance to
ensure that the benefits of these technologies do not exacerbate existing
vulnerabilities or inequalities. Another solution would be for the mobile
computing community to adopt practices for reporting harms and risks
associated with the use of specific mobile or wearable sensors and
technologies. Conferences such as the Neural Information Processing Systems
(NeurIPS) (Ashurst et al., 2020) and the International Conference on Machine
Learning (ICML) have already started mandating statements that include “risks
associated with the proposed methods, methodology, application or data
collection, and data usage”. More recently, Olteanu et al. (2023) argued that
even responsible AI research needs impact statements, too; such statements aim
at disclosing any possible negative consequences, contributing to more
inclusive research. Additionally, researchers have proposed algorithmic impact
assessments (AIAs) as a form of accountability for organizations that build
and deploy automated decision-support systems (Metcalf et al., 2021), and
frameworks for bridging the gap between technical and ethical aspects of AI
systems (Kasirzadeh, 2021).

Risk Assessment Checklist for Mobile Computing: Drawing from similar
initiatives in AI and Responsible AI, we developed a risk assessment checklist
for mobile computing based on: the learnings from developing our semi-
automatic method (§4) and the findings about what makes a use risky or not
(§5). These two translated into a five-section checklist, which aligns with
recently agreed ways of reporting an AI system’s use risks (including the
indented use, risks, mitigations, compliance) (Sherman and Eisenberg, 2023;
Microsoft, 2022; Ada Lovelace Institute, 2022; National Institute of Standards
and Technology, 2023b). However, we do not argue that the checklist is
exhaustive, but rather aims to serve as a starting point for the Mobile HCI
community to facilitate and foster transparency and accountability in the
development and use of mobile and wearable uses.

The first section covers the intended use (Figure 4A). This is informed by the
EU AI Act’s requirements for risk assessment, and the fact that prior work
emphasized the importance of documenting a system’s use in a structured five-
component format (Golpayegani et al., 2023). This format is not only suitable
for risk assessment but also comprehensive enough to be usable by both
technical and non-technical stakeholders (i.e., due to its simple yet compact
structure).

The second section is about the data (Figure 4B). From the thematic analysis
(§5.3), it was evident that the type of data (e.g., sensitive vs. non-
sensitive) that a use handles plays a critical role in determining its risk
level. For example, uses that handle data primarily about non-human subjects
(e.g., animals, as per use #50) and the environment (e.g., heat, sound, and
air quality, as per use #112), they are considered low risk. Conversely, uses
that handle primarily sensitive data (e.g., facial images, as per use #1),
they are considered high risk.

The third section is about the risks (Figure 4C). From both the inspection of
high risk uses (§5.2) and the thematic analysis (§5.3), these uses tend to
involve individuals from vulnerable groups or engage in automatic decision
making aimed at behavioural change. Therefore, by formulating questions to
identify and assess these risks, this section aims to preemptively address and
mitigate potential negative impacts on individuals and society.

The fourth section is about the mitigations of risks associated with the
specific mobile and wearable uses as well as the data they handle (Figure 4D).

Finally, the fifth section is about the regulatory compliance (Figure 4E).
Given that our method is directly influenced by the upcoming enforcement of
the EU AI Act, this section aims to prompt for adherence with existing and
upcoming regulations as well as procedures for verification and regular
audits. Such procedures will be a legal necessity to monitor a use and
determine its risk level.

We foresee that this checklist can be applied across a range of stakeholders
and use cases: _AI practitioners_ can use it to identify and assess risks at
various stages of the product development cycle; _AI teams_ can use it to
raise awareness and facilitate discussions about potential risks, leading to
more comprehensive risk mitigation strategies; _researchers_ can use it to
navigate the publication process and to help the community avoid rejecting
risky technologies that may have positive impact; and _end users_ can use it
to understand the various aspects (and AI’s use) they should consider before
using mobile or wearables.

###  6.4. Limitations and Future Work

We acknowledge three main limitations of our work that call for future
research efforts. First, our method incorporates domain experts into the
assessment process. The two authors who manually validated each prompt’s
output have experience in the design, development, and deployment of mobile
and wearable technologies. While not achieving full automation, it expedites
the experts’ tasks. It systematically compiles a list of uses, presenting them
in an organized manner for rigorous risk assessment. Consequently, experts can
optimize their time, reducing the need for extensive literature reviews to
identify potential uses, risks, and benefits. Similarly, our crowdsourcing
study experienced relatively low participation because, in reality, only a
handful of experts can provide the insights required to validate our method.
At the same time, we chose the majority voting to establish the final
classification among crowdworkers. However, this may present a situation
wherein a majority’s decisions overshadow those of a minority (a phenomenon
also known as the ‘tyranny of the majority” (Read, 2009; Sumrall et al.,
2001)). Future studies could explore ways to form cohorts of experts that are
broadly available and accessible to the research community, offering diverse
perspectives in evaluating LLM-generated classifications.

Second, the significant level of disagreement among the experts themselves
highlights the subjective nature of such assessments. This suggests that even
expert evaluations can be heavily biased by individual experiences, domain
expertise, and cultural values (Schaekermann et al., 2019; Hoth et al., 2016).
The diversity in the cultural and professional backgrounds of our experts,
while enriching the study with varied perspectives, also introduced challenges
in reaching consensus. This observation highlights the need for developing
more robust methods to use expert disagreement constructively.

Third, LLMs may not cover all potential impact areas, particularly emerging
ethical issues (e.g., existential risks) or unforeseen societal consequences,
leading to an oversimplification of risks. This may lead to overly simplistic
approaches in dealing with complex socio-technical AI systems, especially
those that deal with applications domains flagged by the EU AI Act as high
risk (e.g., concerning vulnerable groups, or law enforcement). The output of
such an assessment is highly advisable to be vetted by lawyers and regulatory
experts.

##  7\. Conclusion

We proposed a method for assessing the risks and benefits of AI in mobile and
wearable uses. Using our method, we generated 138 such uses of AI, while also
evaluating their associated risks as defined by the EU AI Act and benefits in
line with the UN Sustainable Development Goals; upon manual validation of our
method, we confirmed its accuracy to be over 85%. We found that a specific set
of mobile computing uses (low risk) primarily benefit the environment and
logistics. Interestingly, another set of uses (high risk) that hold
significant potential in improving well-being, safety, and social equality are
linked to risks involving sensitive data, vulnerable groups, and automated
decision-making. To not dismiss the potential of these uses, we proposed a
checklist for assessing the risks of mobile and wearable uses.

## References

  * (1)
  * Ada Lovelace Institute (2022) Ada Lovelace Institute. 2022.  _Algorithmic Impact Assessment: AIA Template_.  Retrieved January 22, 2024 from https://www.adalovelaceinstitute.org/resource/aia-template/
  * Afzal et al. (2022) Sayed Saad Afzal, Waleed Akbar, Osvy Rodriguez, Mario Doumet, Unsoo Ha, Reza Ghaffarivardavagh, and Fadel Adib. 2022.  Battery-free wireless imaging of underwater environments.  _Nature communications_ 13, 1 (2022), 5546.
  * Allan et al. (2013) Blake M Allan, John PY Arnould, Jennifer K Martin, and Euan G Ritchie. 2013.  A cost-effective and informative method of GPS tracking wildlife.  _Wildlife Research_ 40, 5 (2013), 345–348.
  * Andrachuk et al. (2019) Mark Andrachuk, Melissa Marschke, Charlotte Hings, and Derek Armitage. 2019.  Smartphone technologies supporting community-based environmental monitoring and implementation: a systematic scoping review.  _Biological Conservation_ 237 (2019), 430–442.
  * Aseniero et al. (2020) Bon Adriel Aseniero, Marios Constantinides, Sagar Joglekar, Ke Zhou, and Daniele Quercia. 2020.  MeetCues: Supporting online meetings experience. In _2020 IEEE Visualization Conference (VIS)_. IEEE, 236–240.
  * Ashurst et al. (2020) Carolyn Ashurst, Markus Anderljung, Carina Prunkl, Jan Leike, Yarin Gal, Toby Shevlane, and Allan Dafoe. 2020.  A guide to writing the NeurIPS impact statement.  _Centre for the Governance of AI. URL: https://perma. cc/B5R8-2B9V_ (2020).
  * Azure (2023) Microsoft Azure. 2023.  _Introduction to prompt engineering_.  Retrieved September 8, 2023 from https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering#best-practices
  * Bardram and Matic (2020) Jakob E Bardram and Aleksandar Matic. 2020.  A decade of ubiquitous computing research in mental health.  _IEEE Pervasive Computing_ 19, 1 (2020), 62–72.
  * Barmpatsalou et al. (2018) Konstantia Barmpatsalou, Tiago Cruz, Edmundo Monteiro, and Paulo Simoes. 2018.  Mobile forensic data analysis: Suspicious pattern detection in mobile evidence.  _IEEE Access_ 6 (2018), 59705–59727.
  * Borenstein and Howard (2021) Jason Borenstein and Ayanna Howard. 2021.  Emerging challenges in AI and the need for AI ethics education.  _AI and Ethics_ 1 (2021), 61–65.
  * Brown et al. (2020) Tom B. Brown et al. 2020\.  Language Models Are Few-Shot Learners. In _Proceedings of the 34th International Conference on Neural Information Processing Systems_ (Vancouver, BC, Canada) _(NIPS’20)_. Curran Associates Inc., Red Hook, NY, USA, Article 159, 25 pages.
  * Buriro et al. (2016) Attaullah Buriro, Bruno Crispo, Filippo Delfrari, and Konrad Wrona. 2016.  Hold and sign: A novel behavioral biometrics for smartphone user authentication. In _2016 IEEE security and privacy workshops (SPW)_. IEEE, 276–285.
  * Buçinca et al. (2023) Zana Buçinca, Chau Minh Pham, Maurice Jakesch, Marco Tulio Ribeiro, Alexandra Olteanu, and Saleema Amershi. 2023.  AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms.  arXiv:2306.03280 [cs.HC]
  * Byun et al. (2023) Courtni Byun, Piper Vasicek, and Kevin Seppi. 2023.  Dispensing with Humans in Human-Computer Interaction Research. In _Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI EA ’23)_. Association for Computing Machinery, New York, NY, USA, Article 413, 26 pages.  https://doi.org/10.1145/3544549.3582749
  * Cecchinato et al. (2015) Marta E Cecchinato, Anna L Cox, and Jon Bird. 2015.  Smartwatches: the good, the bad and the ugly?. In _Proceedings of the 33rd Annual ACM Conference extended abstracts on human factors in computing systems_. 2133–2138.
  * Chaudhari et al. (2018) Apurva Chaudhari, Bhushan Mapari, and Shreenivas Jog. 2018.  Effective environmental monitoring & domestic home conditions by implementation of IoT. In _2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)_. IEEE, 1–5.
  * Chen et al. (2023) Siyuan Chen, Mengyue Wu, Kenny Q Zhu, Kunyao Lan, Zhiling Zhang, and Lyuchun Cui. 2023.  LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation.  _arXiv preprint arXiv:2305.13614_ (2023).  https://arxiv.org/abs/2305.13614
  * Chidambaram et al. (2022) Swathikan Chidambaram, Yathukulan Maheswaran, Kian Patel, Viknesh Sounderajah, Daniel A Hashimoto, Kenneth Patrick Seastedt, Alison H McGregor, Sheraz R Markar, and Ara Darzi. 2022.  Using artificial intelligence-enhanced sensing and wearable technology in sports medicine and performance optimisation.  _Sensors_ 22, 18 (2022), 6920.
  * Choi et al. (2021) Jun-Ho Choi, Marios Constantinides, Sagar Joglekar, and Daniele Quercia. 2021.  KAIROS: Talking heads and moving bodies for successful meetings. In _Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications_. 30–36.
  * Chowdhury et al. (2019) Ushashi Chowdhury, Pranjal Chowdhury, Sourav Paul, Anwesha Sen, Partho Protim Sarkar, Shubhankur Basak, and Abari Bhattacharya. 2019.  Multi-sensor wearable for child safety. In _2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)_. IEEE, 0968–0972.
  * Chung et al. (2022) John Joon Young Chung, Wooseok Kim, Kang Min Yoo, Hwaran Lee, Eytan Adar, and Minsuk Chang. 2022.  TaleBrush: Sketching Stories with Generative Pretrained Language Models. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 209, 19 pages.  https://doi.org/10.1145/3491102.3501819
  * Ciolacu et al. (2019) Monica Ionita Ciolacu, Leon Binder, Paul Svasta, Ioan Tache, and Dan Stoichescu. 2019.  Education 4.0–jump to innovation with IoT in higher education. In _2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME)_. IEEE, 135–141.
  * Comission (2021) European Comission. 2021.  _Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts_.  European Comission.  Retrieved July 13, 2023 from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206
  * Comission (2023) European Comission. 2023.  _Amendments adopted by the European Parliament on 14 June 2023 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts_.  European Comission.  Retrieved July 13, 2023 from https://www.europarl.europa.eu/doceo/document/TA-9-2023-0236_EN.html
  * Constantinides et al. (2018) Marios Constantinides, Jonas Busk, Aleksandar Matic, Maria Faurholt-Jepsen, Lars Vedel Kessing, and Jakob E Bardram. 2018.  Personalized versus generic mood prediction models in bipolar disorder. In _Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers_. 1700–1707.
  * Constantinides and Quercia (2022) Marios Constantinides and Daniele Quercia. 2022.  Good Intentions, Bad Inventions: How Employees Judge Pervasive Technologies in the Workplace.  _IEEE Pervasive Computing_ 22, 1 (2022), 69–76.
  * Constantinides et al. (2020) Marios Constantinides, Sanja Šćepanović, Daniele Quercia, Hongwei Li, Ugo Sassi, and Michael Eggleston. 2020.  ComFeel: Productivity is a matter of the senses too.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 4, 4 (2020), 1–21.
  * Cornacchia et al. (2016) Maria Cornacchia, Koray Ozcan, Yu Zheng, and Senem Velipasalar. 2016.  A survey on activity detection and classification using wearable sensors.  _IEEE Sensors Journal_ 17, 2 (2016), 386–403.
  * Corsetti et al. (2019) Barbara Corsetti, Raul Sanchez-Reillo, Richard M Guest, and Marco Santopietro. 2019.  Face image analysis in mobile biometric accessibility evaluations. In _2019 International Carnahan Conference on Security Technology (ICCST)_. IEEE, 1–5.
  * Crager and Maiti (2017) Kirsten Crager and Anindya Maiti. 2017.  Information leakage through mobile motion sensors: User awareness and concerns. In _Proceedings of the European Workshop on Usable Security (EuroUSEC)_.
  * Cusack et al. (2017) Brian Cusack, Bryce Antony, Gerard Ward, and Shaunak Mody. 2017.  Assessment of security vulnerabilities in wearable devices.  (2017).
  * Das Swain et al. (2023) Vedant Das Swain, Lan Gao, William A Wood, Srikruthi C Matli, Gregory D Abowd, and Munmun De Choudhury. 2023.  Algorithmic Power or Punishment: Information Worker Perspectives on Passive Sensing Enabled AI Phenotyping of Performance and Wellbeing. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–17.
  * Diez et al. (2019) Fidel Paniagua Diez, Diego Suárez Touceda, José María Sierra Cámara, and Sherali Zeadally. 2019.  Lightweight access control system for wearable devices.  _IT professional_ 21, 1 (2019), 50–58.
  * Dorai et al. (2020) Gokila Dorai, Shiva Houshmand, and Sudhir Aggarwal. 2020.  Data Extraction and Forensic Analysis for Smartphone Paired Wearables and IoT Devices.. In _HICSS_. 1–10.
  * Dowling and Lucey (2023) Michael Dowling and Brian Lucey. 2023.  ChatGPT for (Finance) research: The Bananarama Conjecture.  _Finance Research Letters_ 53 (2023), 103662.  https://doi.org/10.1016/j.frl.2023.103662
  * Dwork (2008) Cynthia Dwork. 2008.  Differential privacy: A survey of results. In _International conference on theory and applications of models of computation_. Springer, 1–19.  https://doi.org/10.1007/978-3-540-79228-4_1
  * Dziri et al. (2022) Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022.  On the origin of hallucinations in conversational models: Is it the datasets or the models?  _arXiv preprint arXiv:2204.07931_ (2022).  https://arxiv.org/abs/2204.07931
  * Eldan and Li (2023) Ronen Eldan and Yuanzhi Li. 2023.  TinyStories: How Small Can Language Models Be and Still Speak Coherent English?  _arXiv preprint arXiv:2305.07759_ (2023).  https://arxiv.org/abs/2305.07759
  * Fazio et al. (2021) Peppino Fazio, Miralem Mehic, and Miroslav Voznak. 2021.  An innovative dynamic mobility sampling scheme based on multiresolution wavelet analysis in IoT networks.  _IEEE Internet of Things Journal_ 9, 13 (2021), 11336–11350.
  * Fletcher et al. (2010) Richard R Fletcher, Ming-Zher Poh, and Hoda Eydgahi. 2010.  Wearable sensors: Opportunities and challenges for low-cost health care. In _2010 Annual International Conference of the IEEE Engineering in Medicine and Biology_. IEEE, 1763–1766.
  * Floridi et al. (2021) Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke, and Effy Vayena. 2021.  _An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations_.  Springer International Publishing, Cham, 19–39.  https://doi.org/10.1007/978-3-030-81907-1_3
  * Frluckaj et al. (2022) Hana Frluckaj, Laura Dabbish, David G. Widder, Huilian Sophie Qiu, and James Herbsleb. 2022.  Gender and Participation in Open Source Software Development.  _Proceedings of the ACM on Human-Computer Interaction_ 6, CSCW2 (2022), 1–31.  https://doi.org/10.1145/3555190
  * Fuster et al. (2023) Jaime Fuster, Sonia Solera-Cotanilla, Jaime Pérez, Mario Vega-Barbas, Rafael Palacios, Manuel Alvarez-Campana, and Gregorio Lopez. 2023.  Analysis of security and privacy issues in wearables for minors.  _Wireless Networks_ (2023), 1–17.
  * Gentry (2009) Craig Gentry. 2009.  _A fully homomorphic encryption scheme_.  Stanford university.
  * Gilardi et al. (2023) Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023.  ChatGPT outperforms crowd workers for text-annotation tasks.  _Proceedings of the National Academy of Sciences 120 (30)_ (2023).  https://doi.org/10.1073/pnas.2305016120
  * Giray (2023) Louie Giray. 2023.  Prompt Engineering with ChatGPT: A Guide for Academic Writers.  _Annals of Biomedical Engineering_ (2023), 1–5.  https://doi.org/10.1007/s10439-023-03272-4
  * Golpayegani et al. (2023) Delaram Golpayegani, Harshvardhan J. Pandit, and Dave Lewis. 2023.  To Be High-Risk, or Not To Be–Semantic Specifications and Implications of the AI Act’s High-Risk AI Applications and Harmonised Standards. In _Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency_ (Chicago, IL, USA) _(FAccT ’23)_. ACM, New York, NY, USA, 905–915.  https://doi.org/10.1145/3593013.3594050
  * Gor et al. (2017) M Gor, Jayneel Vora, Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar, Mohammad S Obaidat, and Balqies Sadoun. 2017.  GATA: GPS-Arduino based Tracking and Alarm system for protection of wildlife animals. In _2017 international conference on computer, information and telecommunication systems (CITS)_. IEEE, 166–170.
  * Hagendorff (2020) Thilo Hagendorff. 2020.  The ethics of AI ethics: An evaluation of guidelines.  _Minds and machines_ 30, 1 (2020), 99–120.  https://doi.org/10.1007/s11023-020-09517-8
  * Harari (2021) Noah Yuval Harari. 2021.  The world after coronavirus.
  * Haris et al. (2014) Muhammad Haris, Hamed Haddadi, and Pan Hui. 2014.  Privacy leakage in mobile computing: Tools, methods, and characteristics.  _arXiv preprint arXiv:1410.4978_ (2014).
  * Hassel and Özkiziltan (2023) Anke Hassel and Didem Özkiziltan. 2023.  Governing the work-related risks of AI: implications for the German government and trade unions.  _Transfer: European Review of Labour and Research_ 29, 1 (2023), 71–86.  https://doi.org/10.1177/10242589221147228
  * Heidel et al. (2020) Alexandra Heidel, Christian Hagist, et al. 2020\.  Potential benefits and risks resulting from the introduction of health apps and wearables into the German statutory health care system: scoping review.  _JMIR mHealth and uHealth_ 8, 9 (2020), e16444.
  * Helwatkar et al. (2014) Amruta Helwatkar, Daniel Riordan, and Joseph Walsh. 2014.  Sensor technology for animal health monitoring.  _International Journal on Smart Sensing and Intelligent Systems_ 7, 5 (2014), 1–6.
  * Hoth et al. (2016) Jessica Hoth, Björn Schwarz, Gabriele Kaiser, Andreas Busse, Johannes König, and Sigrid Blömeke. 2016.  Uncovering predictors of disagreement: ensuring the quality of expert ratings.  _ZDM_ 48 (2016), 83–95.
  * Ilyas and Ahmad (2020) Qazi Mudassar Ilyas and Muneer Ahmad. 2020.  Smart farming: An enhanced pursuit of sustainable remote livestock tracking and geofencing using IoT and GPRS.  _Wireless communications and mobile computing_ 2020 (2020), 1–12.
  * International Federation of Accountants (2018) International Federation of Accountants. 2018.  _Regulatory divergence: costs, risks, impacts._ IFAC.  Retrieved April 25, 2024 from https://www.ifac.org/_flysystem/azure-private/publications/files/IFAC-OECD-Regulatory-Divergence.pdf
  * Jobin et al. (2019) Anna Jobin, Marcello Ienca, and Effy Vayena. 2019.  The global landscape of AI ethics guidelines.  _Nature machine intelligence_ 1, 9 (2019), 389–399.  https://doi.org/10.1038/s42256-019-0088-2
  * Joshi et al. (2008) Apurva Joshi, I Naga VishnuKanth, Navkar Samdaria, Sumit Bagla, and Prabhat Ranjan. 2008.  GPS-less animal tracking system. In _2008 Fourth International Conference on Wireless Communication and Sensor Networks_. IEEE, 120–125.
  * Kasirzadeh (2021) Atoosa Kasirzadeh. 2021.  Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence. In _Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency_. Association for Computing Machinery.  https://doi.org/10.1145/3442188.3445866
  * Kelly (2014) Heather Kelly. 2014.  _Fitbit recalls activity tracker due to skin rashes_.  CNN.  https://edition.cnn.com/2014/02/21/tech/gaming-gadgets/fitbit-force-recall/
  * Laha et al. (2022) Suprava Ranjan Laha, Binod Kumar Pattanayak, and Saumendra Pattnaik. 2022.  Advancement of Environmental Monitoring System Using IoT and Sensor: A Comprehensive Analysis.  _AIMS Environmental Science_ 9, 6 (2022), 771–800.
  * Lee et al. (2022) Mina Lee, Percy Liang, and Qian Yang. 2022.  CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 388, 19 pages.  https://doi.org/10.1145/3491102.3502030
  * Li et al. (2016) He Li, Jing Wu, Yiwen Gao, and Yao Shi. 2016.  Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective.  _International journal of medical informatics_ 88 (2016), 8–17.
  * Lin et al. (2022) Hsin-Chang Lin, Ming-Jen Chen, and Jung-Tang Huang. 2022.  An IoT-based smart healthcare system using location-based mesh network and big data analytics.  _Journal of Ambient Intelligence and Smart Environments_ Preprint (2022), 1–27.
  * Liu et al. (2023) Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023.  Lost in the middle: How language models use long contexts.  _arXiv preprint arXiv:2307.03172_ (2023).  https://arxiv.org/abs/2307.03172
  * Mann et al. (2003) Steve Mann, Jason Nolan, and Barry Wellman. 2003.  Sousveillance: Inventing and using wearable computing devices for data collection in surveillance environments.  _Surveillance & society_ 1, 3 (2003), 331–355.
  * McDonald et al. (2019) Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019.  Reliability and Inter-Rater Reliability in Qualitative Research: Norms and Guidelines for CSCW and HCI Practice.  _Proceedings of the ACM on Human-Computer Interaction_ 3, CSCW (2019).  https://doi.org/10.1145/3359174
  * McMahan et al. (2017) Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017.  Communication-efficient learning of deep networks from decentralized data. In _Artificial intelligence and statistics_. PMLR, 1273–1282.  https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf
  * McNary and Hunter (2018) Sarah McNary and Aaron Hunter. 2018.  Using Wearable Device Data in a Criminal Investigation: A Preliminary Study. In _2018 European Intelligence and Security Informatics Conference (EISIC)_. IEEE, 85–85.
  * Meegahapola et al. (2023) Lakmal Meegahapola, Marios Constantinides, Zoran Radivojevic, Hongwei Li, Daniele Quercia, and Michael S Eggleston. 2023.  Quantified Canine: Inferring Dog Personality From Wearables. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–19.
  * Metcalf et al. (2021) Jacob Metcalf, Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, and Madeleine Clare Elish. 2021.  Algorithmic impact assessments and accountability: The co-construction of impacts. In _Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency_. Association for Computing Machinery, 735–746.  https://doi.org/10.1145/3442188.3445935
  * Microsoft (2022) Microsoft. 2022.  _Microsoft’s framework for building AI systems responsibly_.  Retrieved January 22, 2024 from https://blogs.microsoft.com/on-the-issues/2022/06/21/microsofts-framework-for-building-ai-systems-responsibly/
  * Miles and Huberman (1994) Matthew Miles and Michael Huberman. 1994.  _Qualitative Data Analysis: A Methods Sourcebook_.  Sage.
  * Mills et al. (2016) Adam J Mills, Richard T Watson, Leyland Pitt, and Jan Kietzmann. 2016.  Wearing safe: Physical and informational security in the age of the wearable device.  _Business Horizons_ 59, 6 (2016), 615–622.
  * Mirjafari et al. (2019) Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino Audia, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K Dey, et al. 2019\.  Differentiating higher and lower job performers in the workplace using mobile sensing.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 3, 2 (2019), 1–24.
  * Miro (2022) Miro. 2022.  _Miro | Online Whiteboard for Visual Collaboration_.  Retrieved August 2022 from https://miro.com/
  * Mitchell et al. (2019) Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019.  Model cards for model reporting. In _Proceedings of the conference on fairness, accountability, and transparency_. 220–229.
  * Mittelstadt et al. (2016) Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. 2016.  The ethics of algorithms: Mapping the debate.  _Big Data & Society_ 3, 2 (2016), 2053951716679679.  https://doi.org/10.1177/2053951716679679
  * Mohamed et al. (2021) Elsayed Said Mohamed, AA Belal, Sameh Kotb Abd-Elmabod, Mohammed A El-Shirbeny, A Gad, and Mohamed B Zahran. 2021.  Smart farming for improving agricultural management.  _The Egyptian Journal of Remote Sensing and Space Science_ 24, 3 (2021), 971–981.
  * Moraes et al. (2021) Thiago Guimarães Moraes, Eduarda Costa Almeida, and José Renato Laranjeira de Pereira. 2021.  Smile, you are being identified! Risks and measures for the use of facial recognition in (semi-) public spaces.  _AI and Ethics_ 1, 2 (2021), 159–172.  https://doi.org/10.1007/s43681-020-00014-3
  * Morris et al. (2015) Sinead E Morris, Jonathan L Zelner, Deborah A Fauquier, Teresa K Rowles, Patricia E Rosel, Frances Gulland, and Bryan T Grenfell. 2015.  Partially observed epidemics in wildlife hosts: modelling an outbreak of dolphin morbillivirus in the northwestern Atlantic, June 2013–2014.  _Journal of the Royal Society Interface_ 12, 112 (2015), 20150676.
  * National Institute of Standards and Technology (2023a) National Institute of Standards and Technology. 2023a.  .  Retrieved May 2023 from https://www.nist.gov/itl/ai-risk-management-framework
  * National Institute of Standards and Technology (2023b) National Institute of Standards and Technology. 2023b.  _The EqualAI Algorithmic Impact Assessment Tool_.  Retrieved January 2024 from https://www.equalai.org/aia/
  * Neethirajan (2017) Suresh Neethirajan. 2017.  Recent advances in wearable sensors for animal health management.  _Sensing and Bio-Sensing Research_ 12 (2017), 15–29.  https://doi.org/10.1016/j.sbsr.2016.11.004
  * Nweke et al. (2019) Henry Friday Nweke, Ying Wah Teh, Ghulam Mujtaba, and Mohammed Ali Al-Garadi. 2019.  Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions.  _Information Fusion_ 46 (2019), 147–170.
  * Obaideen et al. (2022) Khaled Obaideen, Bashria AA Yousef, Maryam Nooman AlMallahi, Yong Chai Tan, Montaser Mahmoud, Hadi Jaber, and Mohamad Ramadan. 2022.  An overview of smart irrigation systems using IoT.  _Energy Nexus_ (2022), 100124.
  * Olteanu et al. (2023) Alexandra Olteanu, Michael Ekstrand, Carlos Castillo, and Jina Suh. 2023.  Responsible AI Research Needs Impact Statements Too.  _arXiv preprint arXiv:2311.11776_ (2023).
  * Ömer Aydın and Karaarslan (2022) Ömer Aydın and Enis Karaarslan. 2022.  OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare.  _SSRN Electronic Journal_ (2022), 22–31.  https://doi.org/10.2139/ssrn.4308687
  * OpenAI (2023) OpenAI. 2023.  _Research on GPT-4 - latest updates._ Retrieved August 7, 2023 from https://openai.com/research/gpt-4
  * Park et al. (2023) Sungkyu Park, Assem Zhunis, Marios Constantinides, Luca Maria Aiello, Daniele Quercia, and Meeyoung Cha. 2023.  Social dimensions impact individual sleep quantity and quality.  _Scientific Reports_ 13, 1 (2023), 1–11.
  * Perez-Pozuelo et al. (2021) Ignacio Perez-Pozuelo, Dimitris Spathis, Emma AD Clifton, and Cecilia Mascolo. 2021.  Wearables, smartphones, and artificial intelligence for digital phenotyping and health.  In _Digital Health_. Elsevier, 33–54.
  * Puntoni et al. (2021) Stefano Puntoni, Rebecca Walker Reczek, Markus Giesler, and Simona Botti. 2021.  Consumers and artificial intelligence: An experiential perspective.  _Journal of Marketing_ 85, 1 (2021), 131–151.
  * Read (2009) James H Read. 2009.  _Majority rule versus consensus: the political thought of John C. Calhoun_.  University Press of Kansas.
  * Rios-Navarro et al. (2016) Antonio Rios-Navarro, Juan Pedro Dominguez-Morales, Ricardo Tapiador-Morales, Manuel Dominguez-Morales, Angel Jimenez-Fernandez, and Alejandro Linares-Barranco. 2016.  A sensor fusion horse gait classification by a spiking neural network on SpiNNaker. In _Artificial Neural Networks and Machine Learning–ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I 25_. Springer, 36–44.
  * Roper et al. (2021) Jenna M Roper, Jose F Garcia, and Hideaki Tsutsui. 2021.  Emerging technologies for monitoring plant health in vivo.  _ACS omega_ 6, 8 (2021), 5101–5107.
  * Saldaña (2015) Johnny Saldaña. 2015.  _The Coding Manual for Qualitative Researchers_.  Sage.
  * Saraceni et al. (2012) Simone Saraceni, Andrea Claudi, and Aldo Franco Dragoni. 2012.  An active monitoring system for real-time face-tracking based on mobile sensors. In _Proceedings ELMAR-2012_. IEEE, 53–56.
  * Saraswat et al. (2022) Deepti Saraswat, Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Gulshan Sharma, Pitshou N Bokoro, and Ravi Sharma. 2022.  Explainable AI for healthcare 5.0: opportunities and challenges.  _IEEE Access_ (2022).
  * Saravia (2022) Elvis Saravia. 2022.  _Prompt Engineering Guide_.  DAIR.AI.  Retrieved August 7, 2023 from https://github.com/dair-ai/Prompt-Engineering-Guide
  * Schaekermann et al. (2019) Mike Schaekermann, Graeme Beaton, Minahz Habib, Andrew Lim, Kate Larson, and Edith Law. 2019.  Understanding expert disagreement in medical data analysis through structured adjudication.  _Proceedings of the ACM on Human-Computer Interaction_ 3, CSCW (2019), 1–23.
  * Schriewer and Bulaj (2016) Karl Schriewer and Grzegorz Bulaj. 2016.  Music streaming services as adjunct therapies for depression, anxiety, and bipolar symptoms: convergence of digital technologies, mobile apps, emotions, and global mental health.  _Frontiers in public health_ 4 (2016), 217.
  * Seshadri et al. (2020) Dhruv R Seshadri, Evan V Davies, Ethan R Harlow, Jeffrey J Hsu, Shanina C Knighton, Timothy A Walker, James E Voos, and Colin K Drummond. 2020.  Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments.  _Frontiers in Digital Health_ (2020), 8.
  * Sharma et al. (2023) Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, David Wadden, Khendra G. Lucas, Adam S Miner, Theresa Nguyen, and Tim Althoff. 2023.  Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction.  _arXiv preprint arXiv:2305.02466_ (2023).  https://arxiv.org/abs/2305.02466
  * Sherman and Eisenberg (2023) Eli Sherman and Ian W. Eisenberg. 2023.  AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures.  arXiv:2309.13176 [cs.AI]
  * Shieh (2023) Jessica Shieh. 2023.  _Best practices for prompt engineering with OpenAI API_.  OpenAI.  Retrieved August 7, 2023 from https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
  * Sjoding et al. (2020) Michael W Sjoding, Robert P Dickson, Theodore J Iwashyna, Steven E Gay, and Thomas S Valley. 2020.  Racial bias in pulse oximetry measurement.  _New England Journal of Medicine_ 383, 25 (2020), 2477–2478.
  * Sumrall et al. (2001) Colin D Sumrall, Christopher A Brochu, and John W Merck. 2001.  Global lability, regional resolution, and majority-rule consensus bias.  _Paleobiology_ 27, 2 (2001), 254–261.
  * Sunehra et al. (2020) Dhiraj Sunehra, V Sai Sreshta, V Shashank, and B Uday Kumar Goud. 2020.  Raspberry pi based smart wearable device for women safety using gps and gsm technology. In _2020 IEEE International Conference for Innovation in Technology (INOCON)_. IEEE, 1–5.
  * Tahaei et al. (2023) Mohammad Tahaei, Marios Constantinides, Daniele Quercia, Sean Kennedy, Michael Muller, Simone Stumpf, Q Vera Liao, Ricardo Baeza-Yates, Lora Aroyo, Jess Holbrook, et al. 2023\.  Human-Centered Responsible Artificial Intelligence: Current & Future Trends. In _Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–4.
  * Tolmeijer et al. (2022) Suzanne Tolmeijer, Markus Christen, Serhiy Kandul, Markus Kneer, and Abraham Bernstein. 2022.  Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 160, 17 pages.  https://doi.org/10.1145/3491102.3517732
  * Torous et al. (2017) John Torous, JP Onnela, and Matcheri Keshavan. 2017.  New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices.  _Translational psychiatry_ 7, 3 (2017), e1053–e1053.
  * Tyagi et al. (2020) Kirti Tyagi, Aabha Karmarkar, Simran Kaur, Sukanya Kulkarni, and Rita Das. 2020.  Crop health monitoring system. In _2020 International Conference for Emerging Technology (INCET)_. IEEE, 1–5.
  * United Nations: Department of Economic and Development (2023) United Nations: Department of Economic and Social Affairs Sustainable Development. 2023.  _The 17 Goals_.  Retrieved November 8, 2023 from https://sdgs.un.org/goals
  * Venkateswarlu et al. (2006) Ronda Venkateswarlu, Choo Hui Wei, Ngiam Li Lian, ET Lim, Zijian Zhu, and Mingjiang Yang. 2006.  Wearable high-tech gear for homeland security personnel. In _Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense V_ , Vol. 6201. SPIE, 382–391.
  * Vurro et al. (2023) Filippo Vurro, Riccardo Manfredi, Manuele Bettelli, Gionata Bocci, Alberto Luigi Cologni, Sandro Cornali, Roberto Reggiani, Edoardo Marchetti, Nicola Coppedè, Stefano Caselli, et al. 2023\.  In vivo sensing to monitor tomato plants in field conditions and optimize crop water management.  _Precision Agriculture_ (2023), 1–21.
  * Wall et al. (2014) Jake Wall, George Wittemyer, Brian Klinkenberg, and Iain Douglas-Hamilton. 2014.  Novel opportunities for wildlife conservation and research with real-time monitoring.  _Ecological Applications_ 24, 4 (2014), 593–601.
  * Wei et al. (2023) Chen Wei, Yun Cheng Wang, Bin Wang, and C.-C. Jay Kuo. 2023.  An Overview on Language Models: Recent Developments and Outlook.  _ArXiv_ abs/2303.05759 (2023).  https://api.semanticscholar.org/CorpusID:257482688
  * Wei et al. (2022a) Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022a.  Finetuned language models are zero-shot learners.  _The International Conference on Learning Representations (ICLR)_ (2022).
  * Wei et al. (2022b) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. 2022b.  Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In _Advances in Neural Information Processing Systems_ , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 24824–24837.  https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf
  * Wong and Kim (2016) Kok-Seng Wong and Myung Ho Kim. 2016.  An enhanced user authentication solution for mobile payment systems using wearables.  _Security and Communication Networks_ 9, 17 (2016), 4639–4649.
  * Wu et al. (2022) Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022.  AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 385, 22 pages.  https://doi.org/10.1145/3491102.3517582
  * Xu and Ma (2023) Xiangyun Xu and Wei Ma. 2023.  The regulation of the body by smart wearable devices and their social risk progression.  _Wearable Technology_ 2, 1 (2023), 60–72.
  * Yang et al. (2018b) Liyun Yang, Ke Lu, Jose A Diaz-Olivares, Fernando Seoane, Kaj Lindecrantz, Mikael Forsman, Farhad Abtahi, and JöRgen AE Eklund. 2018b.  Towards smart work clothing for automatic risk assessment of physical workload.  _Ieee Access_ 6 (2018), 40059–40072.
  * Yang et al. (2018a) Zhice Yang, Qianyi Huang, and Qian Zhang. 2018a.  Backscatter as a covert channel in mobile devices.  _GetMobile: Mobile Computing and Communications_ 22, 1 (2018), 31–34.
  * Zheng et al. (2023) Zhe Zheng, Ke-Yin Chen, Xin-Yu Cao, Xin-Zheng Lu, and Jia-Rui Lin. 2023.  LLM-FuncMapper: Function Identification for Interpreting Complex Clauses in Building Codes via LLM.  _arXiv preprint arXiv:2308.08728_ (2023).
  * Zhou et al. (2023) Ke Zhou, Marios Constantinides, Daniele Quercia, and Sanja Šćepanović. 2023.  How Circadian Rhythms Extracted From Social Media Relate to Physical Activity and Sleep. In _Proceedings of the International AAAI Conference on Web and Social Media_ , Vol. 17. 948–959.
  * Zuboff (2023) Shoshana Zuboff. 2023.  The age of surveillance capitalism.  In _Social Theory Re-Wired_. Routledge, 203–213.

## Appendix

Table 2. List of LLM-generated AI uses for mobile and wearable technologies. Each entry includes an ID, description, an indication of whether it is high risk, the goals it supports, and the specific clauses from the EU AI Act that categorize the use as high risk or not. Use #64 is classified as unacceptable, and was marked with a (*). ID | Description | High Risk | SDG Goals | AIActText
---|---|---|---|---
1 | Enhancing security through facial recognition using high-performance cameras in smartphones | Yes | 16, 5, 8 | Article 6, Amendment 711 - biometric identification/categorization of natural persons
2 | Monitoring and analyzing crowd behavior using mobile cameras | Yes | 16, 11, 5, 8, 3 | Article 6, Amendment 711 - biometric identification/categorization of natural persons
3 | Identifying individuals in criminal investigations using smartphones and wearable watches | Yes | 16, 10, 5, 8, 3, 11 | Article 6, Amendment 711 - biometric identification/categorization of natural persons
4 | Monitoring children’s location for safety using wristbands | Yes | 3, 11, 16, 5, 8 | Amendment 38 - perceive or exploit vulnerabilities of children and people
5 | Monitoring elderly health and activity using wristbands | Yes | 3, 11 | Amendment 38 - perceive or exploit vulnerabilities of children and people
6 | Coordinating family calendar and tasks on mobile phones | No | 5, 8, 12 | N/A
7 | Sharing calendars and reminders on mobile phones | No | 5, 8, 12 | N/A
8 | Sharing location for coordination through smartwatches | No | 11, 5, 3 | N/A
9 | Tracking emotions using mobile app for improving emotional well-being | Yes | 5, 3 | Article 6, Amendment 712 - biometric identification/categorization of natural persons
10 | Tracking health status using smart rings for chronic conditions | Yes | 8, 3 | Article 6 - access to essential private services
11 | Tracking movements using accelerometers for improving physical therapy outcomes | Yes | 8, 3 | Article 6 - access to essential private services
12 | Enhancing mental health treatment using smartwatches | Yes | 8, 3 | Article 6 - biometric identification/categorization of natural persons
13 | Promoting physical fitness through activity tracking on smartwatches | No | 5, 8, 3 | N/A
14 | Tracking sleep quality | No | 8, 3 | N/A
15 | Providing meditation guidance on a mobile app | No | 8, 3 | N/A
16 | Performing gesture recognition using accelerometers for UI accessibility | No | 4, 8, 3, 9 | N/A
17 | Tracking motion and environmental interaction using VR headsets | No | 8, 4, 3 | N/A
18 | Enabling hands-free device control through voice recognition and command execution | No | 8, 4, 3 | N/A
19 | Tracking expenses and monitoring personal finances using a mobile app | No | 12, 5, 8, 4, 1 | N/A
20 | Enabling secure mobile payments and transfers with a smartwatch | Yes | 17, 10, 8, 1 | Article 6, Amendment 64 - AI systems as safety component
21 | Providing investment and financial advice using a mobile app | Yes | 10, 5, 8, 4, 1 | Article 6, Amendment 64 - AI systems as safety component
22 | Delivering interactive educational content using a mobile app | Yes | 4, 8 | Article 6 - Education and vocational training
23 | Providing personalized learning recommendations using a tablet | Yes | 4, 8 | Amendment 715, 716 - assign natural persons to educational and vocational training
24 | Enabling online exams using a 360 degree camera | Yes | 4, 17, 10, 8 | Article 6 - Education and vocational training
25 | Monitoring employee productivity using smart badges | Yes | 8, 4, 5, 16 | Article 6 - AI systems as safety component
26 | Monitoring physical movements for workplace safety using smartwatches | Yes | 3, 8, 5, 16, 11 | Article 6 - AI systems as safety component and for monitoring/evaluating performance
27 | Tracking work hours and tasks during remote work using a mobile app | Yes | 12, 5, 8, 4, 3 | Article 6 - AI systems as safety component and for monitoring/evaluating performance
28 | Tracking travel patterns for public transportation efficiency using smart cards | Yes | 17, 13, 12, 11, 5, 9, 8 | Article 6 - AI systems as safety component
29 | Monitoring health status and symptoms using headbands | Yes | 10, 9, 8, 3 | Amendment 721 - evaluate natural persons’ eligibility for public benefits and services
30 | Tracking eligibility criteria and application status for social benefits using a mobile app | Yes | 10, 8, 5, 3, 1 | Amendment 721 - evaluate natural persons’ eligibility for public benefits and services
31 | Tracking user preferences for personalized online shopping experience using a mobile app | No | 12, 8 | N/A
32 | Customizing music and video recommendations using earbuds | Yes | 12, 8 | Amendment 740 - recommend user-generated content available on the platform
33 | Tailoring news and information feeds using a mobile app | Yes | 5, 4, 8, 12 | Amendment 740 - recommend user-generated content available on the platform
34 | Tracking user interactions for enhancing user engagement using a mobile app | No | 8, 12 | N/A
35 | Facilitating social connections using a mobile app | Yes | 3, 8 | Amendment 740 - recommend user-generated content available on the platform
36 | Monitoring harmful content using a mobile app | Yes | 10, 12, 5, 4, 3 | Amendment 740 - recommend user-generated content available on the platform
37 | Monitoring physical activity and vital signs for improving athletic performance using armbands | No | 5, 3, 4 | N/A
38 | Tracking environmental conditions in outdoor spaces using a mobile app | No | 5, 3, 4, 8, 11, 16 | N/A
39 | Monitoring player health for team management using armbands | No | 5, 3, 4 | N/A
40 | Tracking user motion for enhancing user experience using VR headsets | No | 5, 8, 3, 4 | N/A
41 | Monitoring audience reactions for facilitating interactive performances using mobile cameras | Yes | 5, 8, 4 | Amendment 52, 191 - infer emotions, thoughts, states of mind individuals or groups
42 | Analyzing user preferences for personalized content recommendations using a mobile app | No | 5, 8, 4 | N/A
43 | Preventing unauthorized access through biometric authentication using mobile scanners | Yes | 16, 5, 8 | Article 6, Amendment 711 - biometric identification/categorization of natural persons
44 | Analyzing network traffic for security threats using mobile dashboards | Yes | 16, 5, 8, 9 | AI Article 6, Amendment 713 - AI systems as safety component
45 | Tracking user location and providing emergency alerts for personal safety using smartwatches | Yes | 5, 8, 3, 16, 11 | N/A
46 | Analyzing user behavior for personalized advertising using a mobile app | No | 8 | N/A
47 | Tracking user interactions with ads for customer engagement using a mobile app | No | 8 | N/A
48 | Analyzing marketing campaign performance data using a mobile app | No | 8, 12 | N/A
49 | Monitoring crop health and growth using IoT sensors | No | 17, 11, 13, 9, 12, 2, 15, 8 | N/A
50 | Tracking livestock health and location using GPS collars | No | 13, 12, 17, 11, 9, 2, 15, 8 | N/A
51 | Optimizing irrigation through soil moisture detection using IoT sensors | No | 17, 13, 9, 12, 2, 11, 15, 8, 6 | N/A
52 | Improving customer engagement through personalized recommendations using a mobile app | No | 12, 8 | N/A
53 | Automating task management in business operations using a tablet | No | 12, 17, 9, 8 | N/A
54 | Enhancing product development using customer feedback from mobile apps | No | 12, 8 | N/A
55 | Facilitating autonomous navigation using mobile cameras | No | 11, 9, 8, 15 | N/A
56 | Enabling human-robot interaction using mobile cameras | Yes | 9, 3, 8 | Article 6 - AI as safety component
57 | Improving the quality of multi-sensor data fusion in fall detection smartwatches | Yes | 9, 3, 8 | AI Article 6 - AI systems as safety component
58 | Accelerating scientific discovery through analyzing data from sensor-equipped backpack | No | 17, 12, 9, 8, 4 | N/A
59 | Facilitating remote collaboration using mobile cameras | No | 17, 13, 9, 8, 4 | N/A
60 | Improving research data management using a mobile app | No | 17, 9, 8, 4 | N/A
61 | Monitoring infrastructure health using pressure sensors | Yes | 17, 11, 3, 15, 13, 8, 16, 9, 12, 7 | Article 6 - AI systems for management and operation of critical infrastructure
62 | Predicting infrastructure failures using wearable sensors | Yes | 17, 11, 3, 15, 13, 8, 16, 9, 12, 7 | Article 6 - AI systems for management and operation of critical infrastructure
63 | Optimizing infrastructure operations using tablets | Yes | 17, 11, 3, 15, 13, 8, 16, 9, 6, 12, 7 | Article 6 - AI systems for management and operation of critical infrastructure
64 | Enhancing surveillance capabilities using mobile cameras | * | 16, 5, 8, 11 | Article 5, 6 - remote biometric identification in public spaces used by law enforcement
65 | Improving crime prediction using mobile cameras | Yes | 16, 5, 8 | Article 6 - predict criminal offence based on profiling of natural persons
66 | Facilitating evidence collection using a mobile app | Yes | 16, 5, 8 | Amendment 728 - AI Systems for investigation or prosecution of criminal offences
67 | Enhancing border security using mobile fingerpint scanners | Yes | 16, 5, 8, 17, 10, 11 | Amendment 736 - AI Systems for border control management
68 | Improving asylum application processing using mobile document scanners | Yes | 16, 1, 3, 5, 8, 17, 10, 11 | Amendment 735 - AI Systems for assessment in relation to applications for asylum
69 | Tracking migrant location using GPS | Yes | 16, 5, 17, 10 | Amendment 736 - AI Systems for border control management
70 | Enhancing voter participation using a mobile app | Yes | 16, 4 | Amendment 739 - influence the outcome of election/referendum/voting behaviour
71 | Improving election transparency using a mobile app | Yes | 16, 10, 8, 9 | Amendment 739 - influence the outcome of election/referendum/voting behaviour
72 | Facilitating citizen engagement using digital kiosks | No | 16, 11, 5, 17, 8, 4, 9 | N/A
73 | Enhancing immersive storytelling experiences using smart glasses | No | 4 | N/A
74 | Facilitating real-time language translation using earbuds | No | 4, 8, 10 | N/A
75 | Improving content accessibility using mobile screen readers | No | 16, 8, 4, 9 | N/A
76 | Assisting visually impaired individuals using smart canes | Yes | 11, 16, 4, 10, 8, 3 | Amendment 38 - perceive or exploit vulnerabilities of children and people
77 | Supporting individuals with hearing impairment using earbuds | Yes | 11, 16, 4, 10, 8, 3 | Amendment 38 - perceive or exploit vulnerabilities of children and people
78 | Tracking movement for assisting individuals with mobility issues using accelerometers | Yes | 11, 16, 4, 10, 8, 3 | Article 6 - AI system as a safety component
79 | Monitoring energy consumption using infrared sensors | No | 13, 12, 17, 11, 15, 9, 7 | N/A
80 | Optimizing renewable energy production using heat sensors | No | 13, 12, 17, 11, 15, 9, 7 | N/A
81 | Improving energy efficiency using infrared sensors | Yes | 13, 12, 11, 15, 9, 7 | Article 6 - AI system as a safety component
82 | Enhancing soldier training using VR headsets | Yes | 16, 17, 9, 8, 4 | Article 6 - AI system as a safety component
83 | Improving battlefield situational awareness using smart glassess | Yes | 16, 17, 9, 8, 4 | Article 6 - AI system as a safety component
84 | Enhancing soldier health monitoring using smart belts | Yes | 16, 17, 9, 8, 4, 3 | Article 6 - AI system as a safety component
85 | Facilitating remote court proceedings using a mobile app | Yes | 17, 10, 16, 11, 8, 9 | Amendment 738 – assist in judicial authority in researching and interpreting facts
86 | Improving voter registration and verification using a mobile app | Yes | 17, 16, 10, 11, 9 | Article 6 - AI Systems for administration of justice and democratic processes
87 | Enhancing public participation in democratic processes using smart kiosks | Yes | 17, 16, 10, 11, 5, 8, 4, 9 | Amendment 738 – assist in judicial authority in researching and interpreting facts
88 | Improving public service delivery using a mobile app | Yes | 16, 11, 5, 8, 4, 3, 9 | Amendment 67 - apply for or receive public assistance benefits and services
89 | Enhancing disaster management using accelerometers | Yes | 17, 13, 9, 8, 3, 11, 16, 14, 15 | Amendment 724 - AI systems for dispatching of emergency first response services
90 | Improving traffic management through cameras | Yes | 13, 12, 11, 16, 9 | Article 6 - AI systems as safety component
91 | Facilitating diplomatic communication using earbuds | No | 16, 17, 10, 8, 4 | N/A
92 | Monitoring global protest actions using a mobile app | No | 16, 17, 5, 13, 9, 11 | N/A
93 | Improving international negotiation processes using a mobile app | No | 16, 17, 10, 4 | N/A
94 | Enhancing food quality inspection using mobile cameras | Yes | 2, 12, 3, 17, 11, 9, 8 | Amendment 207 - AI Systems for critical infrastructure
95 | Improving food traceability using RFID tags | Yes | 2, 12, 3, 17, 11, 9, 8 | Amendment 207 - AI Systems for critical infrastructure
96 | Enhancing food safety compliance using mobile cameras | Yes | 2, 12, 3, 17, 11, 9, 8 | Amendment 207 - AI Systems for critical infrastructure
97 | Enhancing disaster response using GPS | Yes | 17, 13, 8, 3, 16, 9, 11 | Amendment 724 - AI systems for dispatching of emergency first response services
98 | Monitoring health status of victims using wristbands | Yes | 3, 9, 8, 11 | Amendment 724 - AI systems for dispatching of emergency first response services
99 | Coordinating rescue operations using a mobile app | Yes | 17, 13, 8, 9, 3, 11, 16 | Amendment 724 - AI systems for dispatching of emergency first response services
100 | Improving aid distribution efficiency using motion sensors | Yes | 12, 17, 16, 11, 1, 2, 3, 13, 8, 9 | Amendment 721 - evaluate natural persons’ eligibility for public benefits and services
101 | Monitoring health status of aid recipients using wristbands | Yes | 17, 3, 9, 8, 11 | Amendment 721 - evaluate natural persons’ eligibility for public benefits and services
102 | Facilitating communication in disaster zones using a mobile app | No | 16, 17, 13, 8, 9, 11 | N/A
103 | Improving fleet management efficiency using GPS | No | 12, 11, 17, 9, 8 | N/A
104 | Enhancing driver safety using motion sensors | Yes | 3, 12, 11, 8, 9, 16 | Amendment 713 - AI Systems for management and operation of road traffic
105 | Optimizing fuel consumption using pressure sensors | No | 13, 12, 11, 9, 7, 15 | N/A
106 | Monitoring urban mobility patterns using GPS | Yes | 13, 12, 11, 16, 9 | Article 6 - AI Systems as safety component
107 | Assessing public space usage using mobile cameras | Yes | 16, 11, 12, 9 | Article 6 - Biometric identification and categorisation of natural persons
108 | Improving public transportation using mobile cameras | Yes | 12, 11, 9 | Article 6 - AI Systems as safety component
109 | Detecting suspicious behavior using motion sensors | Yes | 16, 10, 17, 8, 3, 11 | Article 6 - Biometric identification and categorisation of natural persons
110 | Enhancing surveillance capabilities for counter-terrorism using GPS | Yes | 16, 10, 17, 8, 3, 11, 9 | Article 6 - Biometric identification and categorisation of natural persons
111 | Improving communication in field operations using earbuds | Yes | 17, 16, 8, 9, 4 | Article 6 - AI Systems as safety component
112 | Monitoring environmental conditions using heat sensors | No | 13, 17, 12, 6, 3, 14, 9, 15, 8, 11, 7 | N/A
113 | Tracking wildlife movements using GPS collars | No | 17, 14, 15, 13, 9, 8 | N/A
114 | Assessing resource usage efficiency using motion sensors | No | 13, 12, 17, 11, 9, 6, 7, 15 | N/A
115 | Enhancing cross-border surveillance using mobile fingerprint scanners | Yes | 16, 17, 10, 8, 11, 9 | Amendment 736 - AI Systems for border control management
116 | Improving communication in joint operations using smart glasses | Yes | 16, 17, 8, 9, 4 | Article 6 - AI Systems as safety component
117 | Detecting illegal activities using motion sensors | Yes | 16, 10, 11, 8, 5 | Amendment 209 - AI Systems used for determining social behavior
118 | Monitoring climate change using heat sensors | No | 13, 17, 11, 6, 9, 15, 10, 7 | N/A
119 | Tracking impact of climate change on wildlife using GPS collars and heat sensors | No | 17, 11, 15, 14, 13, 9, 8 | N/A
120 | Assessing effectiveness of energy-saving interventions using smart electricity meters | No | 13, 12, 17, 6, 11, 7, 9, 8, 14, 15 | N/A
121 | Enhancing immersive gaming experience using VR headsets | No | 9 | N/A
122 | Tracking physical activity for improving physical fitness using wristbands | No | 8, 3, 4 | N/A
123 | Facilitating virtual social interaction using VR headsets | No | 4, 8 | N/A
124 | Tracking and improving musical performance using a mobile app | No | 4, 8 | N/A
125 | Monitoring gardening activities using motion sensors | No | 11, 12, 2, 15, 8 | N/A
126 | Enhancing photography skills using mobile cameras | No | 4, 8 | N/A
127 | Improving home security using mobile cameras | Yes | 5, 11 | Article 6 - AI Systems as safety component
128 | Enhancing energy efficiency using heat sensors | Yes | 13, 11, 12, 9, 7 | Article 6 - AI system as a safety component
129 | Facilitating home automation using smart thermostats | Yes | 13, 12, 9 | Article 6 - AI system as a safety component
130 | Improving public safety using mobile cameras | Yes | 11, 5, 8, 3, 16 | Article 6 - AI Systems as safety component
131 | Enhancing accessibility for disabled individuals using smart glasses | Yes | 11, 10, 8, 3 | Article 6 - access to essential private services and public services and benefits
132 | Facilitating disaster response using a mobile app | Yes | 17, 13, 9, 8, 3, 11, 16, 10 | Amendment 724 - AI systems for dispatching of emergency first response services
133 | Improving road safety using mobile cameras | Yes | 3, 11, 9, 8, 16 | Article 6 - AI Systems as safety component
134 | Enhancing navigation and route planning using GPS | No | 12, 11, 8, 9 | Article 6 - AI systems as safety component
135 | Facilitating autonomous driving using motion sensors | Yes | 11, 9, 8 | Article 6 - AI systems as safety component
136 | Enhancing language learning using a mobile app | No | 4, 8 | N/A
137 | Improving remote communication using mobile cameras | No | 4, 8, 9 | N/A
138 | Facilitating social networking using a mobile app | No | 4, 8, 9 | N/A