# FairComp: Workshop on Fairness and Robustness
in Machine Learning for Ubiquitous Computing

Sofia Yfantidou<EMAIL_ADDRESS>0000-0002-5629-3493 Aristotle University
of ThessalonikiThessalonikiGreece ,  Dimitris Spathis  dimitrios.spathis
0000-0001-9761-951X @nokia-bell-labs.com Nokia Bell LabsCambridgeUnited
Kingdom ,  Marios Constantinides  marios.constantinides @nokia-bell-labs.com
0000-0003-1454-0641 Nokia Bell LabsCambridgeUnited Kingdom ,  Tong Xia
<EMAIL_ADDRESS>0000-0002-6994-6318 University of CambridgeCambridgeUnited
Kingdom and  Niels van Berkel<EMAIL_ADDRESS>0000-0001-5106-7692
Aalborg UniversityAalborgDenmark

(2023)

###### Abstract.

How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are
both ethical and fair? While fairness in machine learning (ML) has gained
traction in recent years, fairness in UbiComp remains unexplored. This
workshop aims to discuss fairness in UbiComp research and its social,
technical, and legal implications. From a _social perspective_ , we will
examine the relationship between fairness and UbiComp research and identify
pathways to ensure that ubiquitous technologies do not cause harm or infringe
on individual rights. From a _technical perspective_ , we will initiate a
discussion on data practices to develop bias mitigation approaches tailored to
UbiComp research. From a _legal perspective_ , we will examine how new
policies shape our community’s work and future research. We aim to foster a
vibrant community centered around the topic of responsible UbiComp, while also
charting a clear path for future research endeavours in this field.

fairness, bias, discrimination, responsible AI, ethical AI

††journalyear: 2023††copyright: rightsretained††conference: Adjunct
Proceedings of the 2023 ACM International Joint Conference on Pervasive and
Ubiquitous Computing & the 2023 ACM International Symposium on Wearable
Computing; October 8–12, 2023; Cancun, Quintana Roo, Mexico††booktitle:
Adjunct Proceedings of the 2023 ACM International Joint Conference on
Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on
Wearable Computing (UbiComp/ISWC ’23 Adjunct ), October 8–12, 2023, Cancun,
Quintana Roo, Mexico††doi: 10.1145/3594739.3605107††isbn:
979-8-4007-0200-6/23/10††ccs: Human-centered computing Ubiquitous and mobile
computing††ccs: Applied computing Consumer health††ccs: Social and
professional topics Codes of ethics

##  1\. Background

Due to the integration of Machine Learning (ML) into Ubiquitous Computing
(UbiComp), tasks that were once deemed impossible or reserved exclusively for
humans, are now within technology’s reach. Algorithms running on ubiquitous
devices, such as smartphones and wearables, have been employed to recognize
human activities (Gu et al., 2021), track sleep patterns (Koskimäki et al.,
2018), and detect breathing phases (Shih et al., 2020). Currently, we witness
a surge in high-stakes applications such as diagnosing COVID-19 infections
(Brown et al., 2020), detection of Atrial Fibrillation (AFib) (Lubitz et al.,
2022), and enhancement of cognitive performance (Costa et al., 2019). However,
as with any technological advancement, ethical opportunities and risks come
hand in hand, and, similarly to humans, ML algorithms are susceptible to
biases.

While fairness research has gained popularity in recent years (Mehrabi et al.,
2021; Tahaei et al., 2023), with a dedicated conference and scientific
community (FAccT), fairness in UbiComp remains wildly unexplored (Yfantidou et
al., 2023). Yet, UbiComp applications are equally likely to suffer from
biases. For example, health sensors such as oximeters consistently misclassify
people of color (Sjoding et al., 2020), while client selection in federated
learning incorporates biases against user profiles on wearables of inferior
networking conditions (Zhou et al., 2022). Additionally, ubiquitous data and
models have certain particularities, oftentimes not shared with the broader
scholarly discourse on ML ethics. For example, they typically include small-
scale, proof-of-concept datasets collected in the lab, making it difficult to
extract population-level insights. Also, they are mostly sequential/temporal
in nature, with biases being harder to surface. In other words, while it is
relatively straightforward to distinguish a person’s skin tone from a picture,
it is much harder to do so from sensor measurements; as UbiComp strives to
blend technologies in the background, biases are blended too (Constantinides
and Quercia, 2022). Compounding the problem, the lack of geographical
diversity, a dearth of longitudinal studies, and the under-reporting of
essential study information create “blind spots” in the research on
algorithmic fairness (van Berkel et al., 2023). Finally, given that such
technologies are typically developed in Western countries, they tend to
reflect the intuition, knowledge, and values of “WEIRD” (Western, Educated,
Industrialized, Rich, and Democratic) cultures, potentially limiting their
inclusivity and relevance to other contexts (Septiandri et al., 2023; Linxen
et al., 2021; Yfantidou et al., 2023). However, with a conscious approach, it
is possible to create models that are both robust and fair. As the field of ML
continues to evolve, the UbiComp community needs to stay vigilant, ensuring
that technological advancements are designed and deployed in a responsible and
ethical manner.

To this end, we aim to spark a discussion about the ethical, social,
technical, and legal issues relevant to fair and ethical UbiComp research.
From a social perspective, we look into how fairness research can be
translated into this domain and identify pathways for ensuring that ubiquitous
technologies do not cause any harm or infringe on any individual rights
(Constantinides and Quercia, 2022). From a technical perspective, we intend to
take a closer look at the community’s data collection, processing, and
modeling practices to ideate fairness enhancement and bias mitigation targeted
at UbiComp work. From a regulatory perspective, we set out to understand how
proposed policies, such as the European AI Act, will frame the work of the
community and drive future research. This balance between performance and
fairness is envisaged as a viable way forward—an ideal compromise.

These perspectives raise numerous challenges and questions that we seek to
address in this workshop. How can we leverage existing fairness research and
adapt it to the UbiComp domain? How can we define and quantify fairness in
prevalent data (e.g., time-series) and model (e.g., regression, multi-class
classification) modalities? How can data and labels be acquired ethically? How
can we generate fair synthetic data or recruit representative real-world
samples? How do we incorporate fairness into our technology development
processes and deployment monitoring by design? Essentially, how do we better
equip our community to deal with unfairness?

Besides these questions, which hold significance for the UbiComp community,
several challenges extend to other disciplines, including Philosophy,
Sociology, Law, Psychology, or any of the broad range of subjects contributing
to this area. Which ethical challenges arise when technologies are interwoven
into everyday life until they are indistinguishable from it? Which are the
historical and systemic biases that frame the domain’s research? How can
ubiquitous applications be regulated without stifling innovation?

##  2\. FairComp Workshop

We aim FairComp to be an interdisciplinary forum beyond publications’
solicitation that brings together academia and industry. Notably, we seek to
bring together researchers and practitioners whose work lies within the ACM
SIGCHI domains (e.g., UbiComp, HCI, CSCW), as well as FAccT, ML & AI, Social
sciences, Philosophy, Law, Psychology, and others. Workshop organizers are
actively engaged in the aforementioned themes and will encourage their network
of colleagues and students to participate in the workshop. In particular, the
goal of this workshop is to collaboratively:

  * •

Assess the evolving socio-technical themes and concerns in relation to
fairness across ubiquitous technologies, ranging from health, behavioural, and
emotion sensing to human-activity recognition, mobility, and navigation.

  * •

Map the space of ethical risks and possibilities regarding technological
interventions (e.g., input modalities, learning paradigms, design choices).

  * •

Envision new sensing and data-acquisition paradigms to fairly and accurately
gather ubiquitous physical, physiological, and experiential qualities.

  * •

Explore novel methods for bias mitigation and investigate their suitability
for diverse ubiquitous case studies.

  * •

More generally, start a discourse around the future of “ubiquitous fairness”
and co-create research agenda(s) for meaningfully addressing it.

  * •

Finally, consolidate an international network of researchers to further
develop these research agendas through funding proposals and through steering
future funding instruments.

Relevance and Impact to UbiComp. With its strong community engaged in several
themes (e.g., sensing, HCI, AI/ML), and its synergistic coalitions across
varied domains (Sociology, Philosophy, Health Informatics, Law), UbiComp has a
crucial role in paving the way for responsible, robust, and fair technological
advancements. Coupled with the unique characteristics of ubiquitous
technology, these advancements demand the development of distinct definitions,
metrics, and methodologies to counteract the effects of bias. This calls for
the creation of a subcommunity focused on fairness issues in the domain’s
technology. By raising awareness and advocating decentralized work, we should
encourage every member of the community to integrate fairness considerations
in their research. Collaborating with other disciplines, this workshop aims to
promote scientific exchange and jointly create a comprehensive and effective
framework for ensuring fairness in UbiComp technology.

Long-term Objectives. This workshop will contribute to a deeper understanding
of ethical challenges and opportunities surrounding the robust, and fair use
of ubiquitous technology. Under such efforts, we plan to build an active and
long-lasting community around the workshop’s theme. Finally, we intend to use
the workshop’s momentum, as well as the developed research agendas and their
collaborative follow-ups, to prepare a special issue of a journal (e.g., IEEE
Pervasive) after the conclusion of the workshop. We plan to make an open call
for this issue but will especially invite workshop participants to submit
their work. Furthermore, we will consolidate our workshop’s insights
(including discussion) into an article.

##  3\. Workshop Structure

Workshop Topics. The workshop aims to provide a platform for exchanging ideas
that can shape the future of ubiquitous computing fairness and beyond and to
rethink the role of UbiComp as an enabler of pervasive experiences free from
biases. The main topics of interest include, but are not limited to:

  * •

New definitions, metrics, and criteria of fairness and robustness, tailored
for ubiquitous computing

  * •

Indirect notions of fairness on devices (e.g., unfair resource allocation,
energy, connectivity)

  * •

New methods for bias identification and mitigation

  * •

Bias, discrimination, and measurement errors in data, labels, and under-
represented input modalities

  * •

New benchmark datasets for fairness and robustness evaluation (e.g., sensor
data with protected attributes)

  * •

Geographical equity across datasets and applications (e.g., WEIRD research,
Global South)

  * •

New user study methodologies beyond conventional protocols (e.g., Fairness-by-
Design)

  * •

Robustness (e.g., out-of-distribution generalization, uncertainty
quantification) of ML models in high-stake and real-world applications

  * •

Investigation of fairness trade-offs (e.g., fairness vs. accuracy, privacy,
resource efficiency)

  * •

Implications of regulatory frameworks for UbiComp

Workshop Format. We plan for an open, full-day workshop with 2 invited
keynotes and 10 accepted papers that include completed and ongoing original
empirical works, case studies, reviews, as well as position papers (subject to
# of submissions). All papers will be presented as talks, including Q&A to
allow researchers to engage in discussion with the workshop attendees. To
further engage workshop participants, FairComp will include two interactive
activities: a collaborative ideation session, where participants will be split
into small groups to discuss the ethical, social, technical, and legal
perspectives of UbiComp fairness under the guidance of invited experts and
driven by questions provided by the organizers; and an interactive panel on
_“Ethical & Responsible UbiComp: A Case for Fairness and Robustness”_ with
keynote speakers and industry experts to further discuss reflections on their
work with an open Q&A session with the audience, moderated by one of the co-
organizers. The workshop will take place on-site, with accommodation for
exceptional cases’ remote attendance via Zoom (https://zoom.us/).
Additionally, Slack (https://slack.com/) will be used for facilitating social
interaction. The entire workshop is estimated to last around 8 hours as
illustrated in Table 1.

[Table showing proposed schedule]Table showing the proposed schedule of the
full-day virtual workshop.

Table 1. Proposed schedule for the FairComp workshop.

| Time |  Activity
---|---|---
Session I | 09:00–09:15 |  Welcome: Introduce organizers, participants, workshop objectives, and schedule.
09:15–10:15 |  Keynote #1: Presentation by an invited expert
(45-min talk followed by 15-min Q&A).
10:15–10:30 |  Paper presentations #1: 2 paper presentations
(5-min talk followed by 2-min Q&A).
10:30–10:45 |  Short Break
10:45–12:00 |  Interactive Activity: Collaborative ideation session in small groups about the ethical, social, technical, and legal perspectives of UbiComp fairness.
12:00–13:00 |  Lunch Break
Session II | 13:00–14:00 |  Keynote #2: Presentation by an invited expert
(45-min talk followed by 15-min Q&A).
14:00–14:30 |  Paper presentations #2: 3 paper presentation
(5-min talk followed by 2-min Q&A).
14:30–14:45 |  Short Break
14:45–15:30 |  Paper presentations #3: 5 paper presentation (5-min talk followed by 2-min Q&A).
15:30–15:45 |  Short Break
15:45–16:30 |  Panel Discussion: Keynote speakers and invited industry experts discuss the topic of “Ethical & Responsible UbiComp: a case for fairness and robustness”.
16:30–17:00 |  Wrap Up: Closing remarks and best paper award.

Session I will begin with the first keynote. We contacted Prof. Flora Salim
(University of New South Wales, Australia), who kindly agreed to share lessons
about fairness and robustness on UbiComp. The first half will continue with
paper presentations. It will conclude with an interactive activity aimed at
sparking a discussion around UbiComp fairness perspectives among the
participants while building future collaborations. Session II will start with
the second keynote. Prof. Ricardo Baeza-Yates (Institute for Experiential AI
of Northeastern University, USA) agreed to talk about computational fairness
and human-centric computing. The remaining paper presentations will follow,
along with a panel discussion (Dr Akhil Mathur from Meta AI agreed to be the
industry expert panellist) on the topic to consolidate ideas into an
executable research agenda. The workshop will wrap up with the best paper
award.

Estimated Number of Participants. The growing interest in the workshop’s theme
is demonstrated by the proliferation of similar workshops at other prestigious
conferences, including FairUMAP at UMAP, Trustworthy and Socially Responsible
ML (TSRML) at NeurIPS, and Trustworthy ML in Healthcare at ICLR. Yet, none of
these workshops focused on the particularities of ubiquitous technologies, nor
did they target the UbiComp community. We consider 30-40 attendees an
appropriate size for our workshop, allowing us to shape a comprehensive future
research agenda, build collaborations, and consolidate an active community
around the workshop theme.

Paper Selection and Publication. Submissions will be reviewed by at least two
or three reviewers (including organizers and external reviewers) and will be
published by ACM. Our acceptance criteria will be a mixture of relevance,
novelty, provocativeness, and research quality. Given the timely theme of this
workshop, we are confident of attracting a large number of paper submissions
which will enable us to organise a high-quality workshop.

Important Dates.

  * •

Call for Papers (CfP): 18 April 2023

  * •

Paper submission: 23 June 2023

  * •

Notification to authors: 8 July 2023

  * •

Camera-ready deadline: 31 July 2023

  * •

Workshop Day: 9 October 2023

Pre-workshop Activities. We will disseminate the call for papers (CfP) through
diverse channels, including mailing lists, our social and professional
networks, local ACM chapters, the dedicated workshop website, and our
respective institutional communication channels. The website
(https://faircomp-workshop.github.io/2023/) will be a key platform to
disseminate information, including the CfP, crucial deadlines, profiles of the
co-organizers, Technical Program Committee (TPC), workshop schedule, and
activities. Moreover, the website will also serve as an archive of the
workshop outcomes, containing the workshop’s summary and other outputs.

Upon acceptance, we will reach out to experts from academia and industry to
compose a TPC to review and select author contributions and facilitate
preparing a diverse and thematically-rich program. In constituting the TPC, we
will aim to find a balance among the themes relevant to the workshop. We will
invite submissions of different kinds, ranging from technical papers, work-in-
progress, and reviews to position papers, provocations, and case studies. The
submissions will be 4–6 pages long, including references, and organizers will
provide a template on the website.

Post-workshop Activities. During and following the workshop, the results and
outcomes will be blogged on the workshop website and disseminated in ACM
Interactions. Drawing on the workshop submissions and interactive activities,
we will propose a journal special issue (e.g., IEEE Pervasive) and encourage
participants to collaborate on submissions around the developed research
agendas.

Diversity Statement and Accessibility. Our workshop is committed to creating a
welcoming and inclusive environment for all attendees, regardless of race,
gender, sexual orientation, religion, or ability. This belief will be
implemented by ensuring the diverse selection of organizers, TPC members, and
speakers, disseminating the CfP in mailing lists targetting under-represented
communities in computing, promoting inclusive language, and disseminating the
conference’s code of conduct. We aim to make our workshop inclusive to diverse
participants, including access to materials. We plan on ensuring accessibility
throughout the workshop. Prior to submission, the authors will be asked to
adhere to UbiComp’s Accessible Submission
Guidelines.111https://www.ubicomp.org/ubicomp-
iswc-2023/accessibility/accessibility-guidelines/ Finally, in the weeks
leading up to the workshop, we will conduct a survey with attendees to
identify the accessibility needs for in-person participation to accommodate
during the workshop in collaboration with the JEDI Chairs.

##  4\. Organizers

Sofia Yfantidou is an early-stage researcher at the Aristotle University of
Thessaloniki, and a Marie Skłodowska-Curie fellow at the Innovative Training
Network “Real-time Analytics for the Internet of Sports”. She works at the
intersection of UbiComp and ML fairness. Her current research focuses on
defining, quantifying, and mitigating biases in data and ML models for health
and well-being. She is a Heidelberg Laureate Forum alumna and a Grace Hopper
scholar. Website: https://www.linkedin.com/in/sofiayfantidou/

Dimitris Spathis is a research scientist at Nokia Bell Labs, Cambridge (UK)
and a visiting researcher at the University of Cambridge. His work enables AI
to make the most out of real-world multimodal and sequential data through
label-efficient and robust ML. He previously worked at Microsoft Research,
Telefonica Research, Ocado, and Qustodio. His experience as an organizer of
scientific meetings includes WellComp at Ubicomp ’22, CHIL ’23, Federated
Sensing tutorial at MobiCom ’21, and ML4H co-located with NeurIPS ’22.
Website: https://www.cl.cam.ac.uk/~ds806/

Marios Constantinides is a Senior Research Scientist at Nokia Bell Labs,
Cambridge (UK) and a visiting researcher at the University of Cambridge. He
works in the areas of human-computer interaction, UbiComp, and responsible AI.
His current research focuses on building AI-based technologies that augment
people’s interactions and communication, with a particular focus on the
workplace. He has been a member of the organizing committee of the SensiBlend
workshop at UbiComp ’21, and co-organized two Special Interests Groups (SIG)
at CHI ’23 on future of work and responsible AI. Website:
https://comarios.com/

Tong Xia is a third-year PhD candidate at the University of Cambridge. Her
research interests lie in data mining, ML, and UbiComp for public health and
human well-being. Particularly, she is keen to develop data-efficient, high-
performance, uncertainty-aware, and privacy-preserving mobile health systems.
She previously worked at Tecent, and she has been a committee member of the
UK-Tsinghua association. She also served as the Posters&Demos session chair in
UbiComp ’22. Website: https://xtxiatong.github.io

Niels van Berkel is an Associate Professor at Aalborg University. His work
focuses on the design and evaluation of intelligent computing systems,
particularly in real-world contexts, publishing in HCI, Social Computing, and
Ubiquitous Computing. He has previously served as organiser of workshops at
UbiComp (UbiTtention’20, Mobile Human Contributions ’18, Sensors & Behaviour
’18) and CHI (2VT ’21, Emergent Interaction ’21), and served on the editorial
board for IJHCS (2019–present) and ACM TiiS Special Issue on Human-Centered
Explainable AI. Website: https://www.nielsvanberkel.com/

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