# Mapping the individual, social, and biospheric impacts of Foundation Models

Andrés Domínguez Hernández<EMAIL_ADDRESS>0000-0001-7492-7923 The
Alan Turing InstituteLondonUKNW1 2DB ,  Shyam Krishna  0009-0008-4370-4725
<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK ,  Antonella Maia
Perini<EMAIL_ADDRESS>0000-0002-6526-1784 The Alan Turing
InstituteLondonUKNW1 2DB ,  Michael Katell  0000-0003-2200-6246
<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK ,  SJ Bennett
0000-0002-8520-3194<EMAIL_ADDRESS>University of DurhamDurhamUK ,
Ann Borda  0000-0003-3884-2978<EMAIL_ADDRESS>The Alan Turing
InstituteLondonUK ,  Youmna Hashem  0009-0008-2339-1805<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK ,  Semeli Hadjiloizou  0009-0008-0178-697X
<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK ,  Sabeehah
Mahomed  0000-0002-5726-4432<EMAIL_ADDRESS>The Alan Turing
InstituteLondonUK ,  Smera Jayadeva  0000-0002-2050-9731
<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK ,  Mhairi Aitken
0000-0002-4654-9803<EMAIL_ADDRESS>The Alan Turing InstituteLondonUK and
David Leslie  0000-0001-9369-1653<EMAIL_ADDRESS>Queen Mary University of
LondonLondonUK The Alan Turing InstituteLondonUK

(2024)

###### Abstract.

Responding to the rapid roll-out and large-scale commercialization of
foundation models, large language models, and generative AI, an emerging body
of work is shedding light on the myriad impacts these technologies are having
across society. Such research is expansive, ranging from the production of
discriminatory, fake and toxic outputs, and privacy and copyright violations,
to the unjust extraction of labor and natural resources. The same has not been
the case in some of the most prominent AI governance initiatives in the global
north like the UK’s AI Safety Summit and the G7’s Hiroshima process, which
have influenced much of the international dialogue around AI governance.
Despite the wealth of cautionary tales and evidence of algorithmic harm, there
has been an ongoing over-emphasis within the AI governance discourse on
technical matters of safety and global catastrophic or existential risks. This
narrowed focus has tended to draw attention away from very pressing social and
ethical challenges posed by the current brute-force industrialization of AI
applications. To address such a visibility gap between real-world consequences
and speculative risks, this paper offers a critical framework to account for
the social, political, and environmental dimensions of foundation models and
generative AI. Drawing on a review of the literature on the harms and risks of
foundations models, and insights from critical data studies, science and
technology studies, and environmental justice scholarship, we identify 14
categories of risks and harms and map them according to their individual,
social, and biospheric impacts. We argue that this novel typology offers an
integrative perspective to address the most urgent negative impacts of
foundation models and their downstream applications. We conclude with
recommendations on how this typology could be used to inform technical and
normative interventions to advance responsible AI.

risks, harms, foundation models, AI governance, responsible AI

††journalyear: 2024††copyright: rightsretained††conference: The 2024 ACM
Conference on Fairness, Accountability, and Transparency; June 3–6, 2024; Rio
de Janeiro, Brazil††booktitle: The 2024 ACM Conference on Fairness,
Accountability, and Transparency (FAccT ’24), June 3–6, 2024, Rio de Janeiro,
Brazil††doi: 10.1145/3630106.3658939††isbn: 979-8-4007-0450-5/24/06††ccs:
Social and professional topics Socio-technical systems††ccs: Applied computing
Sociology††ccs: General and reference General literature

##  1\. Introduction

Since the release of ChatGPT at the end of 2022, there has been considerable
and sustained interest across policy, academia, and public discourses in the
risks posed by artificial intelligence (AI), and particularly generative AI.
Though ChatGPT may have generated the most column inches, its underpinning
model—GPT 3.5 and, subsequently, GPT 4—was just one example of wider
developments in the area of so-called “foundation” or “frontier” models.

Foundation models are AI technologies trained on very large, “broad” datasets
that can be applied to a wide range of tasks and purposes (Bommasani et al.,
2022). Their general, unspecified purpose and widespread deployment leads to
concerns about potential unanticipated impacts when used in novel areas. These
models are considered to form the “foundation” or base architecture of other
systems. For example, a number of new applications such as ChatGPT and the
conversational features of the Bing search engine, have been built on top of
successive versions of OpenAI’s GPT foundation model, which has been designed
for natural language processing tasks.

While the techniques involved in developing and deploying foundation models
are not new, the scale of the data used for training, the coordination of
global networks of labor to process these massive amounts of data, the
increased architectural complexity, and the development of increasingly
powerful processors and other computational resources have together made
possible new levels of predictive and generative sophistication. Due to the
resources required to produce foundation models, the most prominent ones known
to exist are the products of large and well-financed technology companies,
such as Anthropic, Cohere, Hugging Face, Meta, OpenAI, Microsoft, and Alphabet
(the latter two being substantial funders of other firms).

Responding to the rapid rollout and large-scale commercialization of
foundation models, large language models (LLMs), and generative AI, an
emerging body of work is exploring the myriad impacts these technologies are
having across society. Such research is expansive. Prominent topics range from
the production of discriminatory, fake, and toxic outputs and privacy and
copyright violations to the unjust extraction of labor and natural resources.
Foundation models present complex issues for law, policy, and practice, which
arise concretely from the intertwined array of socio-technical systems in
which their design, development, and use are embedded. These technologies pose
risks and hazards that emerge from real-world contexts. Such issues arise from
the global supply chains and labor sources that support their production, as
well as the market forces and regulatory environments that influence their
funding and financing. Risks likewise arise from the sociohistorical realities
and legacies of inequity and exclusion that shape the data on which they are
trained and from the patterns of privilege and socioeconomic stratification
that influence the composition of the project teams that build them. All these
as real-world contexts lead to the real-world consequences that have been the
subject of much critical and responsible AI research on the impact of
foundation models and generative AI over the past year. Yet, despite the
importance of centering this empirically oriented work, much of the agenda-
setting policy and public discourse emerging primarily from the main
geographical centres of innovation on foundation models (and relatedly LLMs
and generative AI) has focused on sensational, catastrophic, and largely
speculative risks relating to extreme and hypothetical scenarios.

The narrative of catastrophic risks was amplified by much mainstream, English
language, media coverage of foundation models throughout 2023, fueled by high
profile statements declaring potential existential threats from AI (Metz,
2023b, a). Most notably a statement published in May 2023, and signed by many
prominent figures in the field of AI, claimed that “Mitigating the risk of
extinction from AI should be a global priority alongside other societal-scale
risks such as pandemics and nuclear war” (Center for AI Safety, [n. d.]).
While the research behind these claims is scant and broadly disputed, this
narrative has reached far into predominant policy and regulatory discussions,
including high profile international initiatives in the global north like the
UK’s AI Safety Summit (GOV.UK, 2023) and the G7’s Hiroshima AI Process (OECD,
2023a). Such processes, while presenting valuable opportunities to bring
together the international community in discussing the extant risks that AI
presents to people, society, and the planet, and corresponding approaches to
needed regulation, have been dominated by technical discourses with narrow
framings of “safety” and have prioritized discussions of speculative global
catastrophic or existential risks of AI.

Concerns about this narrowing of AI policy and regulatory discourse have been
echoed by various academic communities who have questioned the lack of
“ideological and demographic diversity” of the AI safety field (Lazar and
Nelson, 2023); and the privileging of speculative narratives as effective
distractions from existing and well-documented risks and harms of AI systems
today (Vallor and Luger, 2023; Helfrich, 2024; Gebru and Torres, 2024). A
wealth of evidence demonstrates in particular the disproportionate negative
impacts that AI systems have on marginalized communities—communities who are
also underrepresented in decision-making processes regarding the design,
development, deployment, or governance of AI. These issues, which are linked
to historical and entrenched power asymmetries, are now being exacerbated by
AI technologies, and risk becoming increasingly more urgent.

Therefore, through a review of the literature on the harms and risks of
foundation models and generative AI, in this paper, we aim to address this
visibility gap between real-world consequences and speculative risks. We
propose a critical framework capable of adequately illuminating socio-
technical risk contexts and attending to the social, political, and
environmental dimensions of foundation models and generative AI. Through
reviewing the existing evidence base our research maps a diverse set of risks
and harms impacting individuals, communities, society, and the biosphere.

The paper starts with a discussion on the theoretical framework employed to
investigate risks and harms in the context of foundation models. This
framework abductively informs the methodological approach, which is detailed
in section 3. Section 4 present our findings by delineating three distinct
levels of algorithmic impacts—individual, social and biospheric. We conclude
discussing how the integrative perspective we offer can aid in understanding
the negative impacts of foundation models, and can help shape responsible AI
futures.

##  2\. Theoretical Lens: Expanding views on algorithmic risks and harms

There is a growing and multidisciplinary body of literature which explores the
potential risks and harms posed by the relatively recent widespread
popularization of foundation models. Although these risks and harms share many
similarities with those presented by other types of AI, and indeed other data-
intensive technologies more generally, it is crucial to examine how the
proliferation and widespread adoption of foundation models may introduce new
challenges, as well as further complicate existing ones.

The concept of risk is pervasive in modern societies and has become a central
aspect of the AI governance discourse. Sociological studies have argued that
theories of risk management and securitization emerged as a means to grapple
with the uncertainties of modernity (Beck, 1992) and “making the future secure
and certain” (Schuilenburg, 2017). The understanding of risk and harm, as well
as how they interrelate, varies greatly across domains. For legal, policy,
organizational, or actuarial purposes, risk is typically approached through
quantifiable measures and anticipatory models of potential adverse or harmful
events such pandemics, financial losses, health and safety incidents, or
operational disruptions (Dionne, 2013; Power, 2009; NIST, 2023; Mäntymäki et
al., 2022). Risk is therefore not inherently an issue of morality and can be
understood as a context-dependent and relational concept—that is, expressed in
relation to how factors like technical developments, organizational
structures, or innovation may adversely impact a desired future and be
variously interpreted by different actors (von Scheve and Lange, 2023). For
example, consider a company which uses a job applicant screening software and
has discovered that it makes racially biased screening recommendations. Where
policy makers and civil society may be concerned about the various ways in
which the technology undermines individual rights and social equity, a company
might instead be more likely to focus on the risk of reputational damage and
how this could in turn result in public distrust, low technology adoption, and
commercial impacts.

Critical social science perspectives underline the multiple ramifications of
identifying, analyzing, and managing risk associated with the impacts of
technology in society (Stahl et al., 2022; Wirtz et al., 2022). In this, some
areas of research critically examine the various ways in which the risks and
harms resulting from data and algorithmic systems are patterned across society
at different levels (Lupton, 2013; Canning and Tombs, 2021). For instance, the
field of science and technology studies (STS) has a well-established body of
work exploring the interdependencies between technology and society,
challenging the widespread deterministic view of technology as operationally
autonomous and inherently value-neutral, and of innovation as following a
predictable linear trajectory (MacKenzie and Wajcman, 1999). When it comes to
examining the impacts of AI, scholars in the fields of STS, critical data
studies, and socio-legal studies have proposed to conceptualize the harmful
effects of data-intensive technologies as issues relevant to human rights,
social justice, cultural institutions, the rule of law, the public sphere,
economic systems, and the environment (Malik et al., 2022; Crawford, 2021;
Williams and Edge, 1996; Marjanovic et al., 2022; Stoilova et al., 2021;
Domínguez Hernández et al., 2023; Smuha, 2021). It is also from a critical
perspective that some scholars increasingly highlight the need to approach
algorithmic governance not only in terms of speculative notions of risk, but
also in response to actual and observed harms to humans and the environment
(Ferri and Gloerich, 2023).

To build an understanding of risks and harms of technology from a moral and
ethical standpoint, it is key to consider the questions of how power is
distributed across the entire ecosystem of actors, where and how power
manifests, and how asymmetries of power and information relate to differential
impacts in society. Here it is helpful to draw insights from feminist and data
justice scholarship, socio-legal studies, and the environmental justice
literature. In particular, the concept of intersectionality makes explicit how
social hierarchies manifest in different outcomes and experiences depending
not only on the identity of the impacted individual or group, but also on the
multiple compounding intersections of social identity and standing. These
intersections go beyond crude constructions and classifications of individuals
in society, to recognize the ways in which nuances of context, identity, and
circumstance distinctly shape people’s understanding and experiences of harm
(Crenshaw, 1991).

To capture these differential impacts as they relate to the proliferation of
foundation models, in this paper we propose, following Leslie and Rossi
(Leslie and Rossi, 2023), to assess risks and harms according to their
individual, social, and biospheric levels of impact, contributing to an
emerging dialogue within the FAccT and AI ethics communities (Simbeck, 2022;
Weidinger et al., 2022). This perspective allows for a nuanced understanding
of the observed and anticipated impacts of foundation models by recognizing
the inherent socio-technical and globally entangled nature of these
technologies. We use this framework to map the risks and harms found in the
literature to date and offer an integrative perspective to address the most
urgent negative impacts of foundation models and their downstream
applications.

##  3\. Methods: snowball and structured search

We conducted a review of literature using two complementary and reinforcing
qualitative literature review approaches which have been shown to be useful in
studies aimed at surfacing patterns and trends in corpora of academic research
(Wohlin et al., 2022).

We first applied a snowball approach (Wohlin, 2014), crowd-sourcing among all
the co-authors, a core set of recent papers focused broadly on algorithmic
harms and risks (Bender et al., 2021; Birhane et al., 2023; Bommasani et al.,
2022; Derczynski et al., 2023; Jones and Steinhardt, 2022; Kasirzadeh and
Gabriel, 2023; Shelby et al., 2023; Weidinger et al., 2022). These papers were
selected based on our inclusion criteria of covering a broad range of
multidisciplinary literature addressing risks and harms associated with
foundation models. Populating a Zotero database, we followed snowball sampling
approaches starting from the set of core articles and then carrying out a
manual search using the references in those articles. Forward snowballing was
used to identify new papers that focus on algorithmic risks and harms citing
those in the core list using Google Scholar and Internet-based searches to
access the abstract and full-text. We identified a total of 64 articles
including journal articles, conference papers, and pre-prints drawing broadly
on technical and socio-technical literature in which key risks and issues of
foundation models, LLMs, and generative AI are described historically and
contemporaneously. The search inclusion criteria covered English language
publications and the most recent publication was of October 2023.

Complementing the snowball approach, we conducted a structured database search
to identify primarily academic articles with a focus on the risks and harms
associated with foundation models, LLMs, and generative AI.

Inclusion criteria covered English language journal articles, including
conference papers and pre-prints, but excluding grey literature, monographs,
commentaries, correspondences, and opinion pieces. The following electronic
databases were searched: arXiv, ACM Digital Library, IEEE, Scopus, and Web of
Science.111The ACL Anthology database was not used as a source for the search
given this database’s core focus on computational linguistics and natural
language processing. After the removal of duplicates and non-relevant papers,
there were 114 papers in total for analysis, of which 11 were also papers in
the snowball search results. The resulting set of papers were published in the
period January 2018 to July 2023. The start date aligns with the date of
appearance in the literature of transformer architectures which underpin LLMs,
such as the BERT large language representation model (Vaswani et al., 2017).

We applied an abductive approach (Tavory and Timmermans, 2014; Vila-Henninger
et al., 2022) to analyze the combined corpus of literature 167 papers. The
abductive approach was used to code the results and subsequently map key
relationships between the risks and harms found in the literature guided by
our theoretical framework described in Section 2. Firstly, we extracted
applicable keyword information from the abstract of each primary study as part
of an initial coding process. The resulting keywords served largely as
summative and process attributes of a risk or type of harm relevant to
foundation models or LLMs. Three researchers subsequently clustered these
attributes and assigned them into parent categories. The codes were
subsequently refined by all co-authors (see Appendix for search strategy). In
this stage, we considered whether at least one of the risks and harms listed
in the paper implied or directly referred to individual, social, or biospheric
impacts to map the papers onto our three-level framework. These codified
findings are further analyzed in Section 4.

###  3.1. Limitations

We note that our structured search showed a prevalence of preprint papers
(non-peer review) in the result set, some of which had been cited extensively
in both preprint and peer-review articles.222We made a distinction between
citations of preprints referenced by other preprints, some of which exceeded
50 citations, and those referenced by peer-reviewed publications. Those
preprints that were more extensively cited had citations in both preprints and
peer-reviewed articles. We used citations only as a broad indication of the
preprints’ impact. While some preprints were highly cited, these may have not
undergone a rigorous peer-review process, and their findings should be
interpreted with caution. In our sample, we analyze articles solely as an
indicator of the range of concerns raised in the existing literature. The
selected papers were also limited to the English language, and date range of
January 2018 to July 2023. This range by default provided only a snapshot of a
growing and multidisciplinary body of work which is arising in response to the
fast development and roll-out of foundations models and LLMs, especially
generative AI related studies. In addition, our structured literature search,
which is not intended as systematic, only centred academic articles as a
relevant account of the discourse on risks and harms of foundation models and
LLMs to inform governance. We also note that this body of research is not
focused exclusively on risks and harms, but often addresses, or points to,
mitigations and governance of foundation models and AI at large. An analysis
of other non-academic and informal sources was not part of this study and it
warrants additional research. Notwithstanding, the codified mappings provide a
substantive view of the research landscape and the range of concerns that are
being raised around foundation models and LLMs, their development,
implementation, and applications.

##  4\. Mapping individual, social, and biospheric impacts of foundation
models

The result set of 14 risks and harms coding (Table 1) is telling of the wide-
ranging and multifaceted challenges posed by foundation models and AI systems
at large. Papers typically engage with three or more aspects, exemplifying the
broad spectrum of concerns in the literature to date. For instance, Zhuo et
al. (Zhuo et al., 2023b) delve into potential biases, toxicity, and issues
related to the reliability and robustness of ChatGPT. Notably, a substantial
set of search results focuses on bias and discrimination. These are most
typically related to LLMs, and include works such as van der Wal et al. (van
der Wal et al., 2023) and Huang et al. (Huang et al., 2023b), which address
multiple biases and harmful stereotypes, and others, like Abid et al. (Abid et
al., 2021), Felkner et al. (Felkner et al., 2023), Ovalle et al. (Ovalle et
al., 2023), and Gadiraju et al. (Gadiraju et al., 2023) that concentrate on
specific societal biases. The result set also reveals that papers that focus
on specific or a limited number of risks and harms often scrutinize issues of
unreliable performance of foundation models, or misinformation and propaganda.
While other issues such as lock-in and opacity, or overdependence in human-
computer interaction, are less prevalent in the result set, it is important to
note that their absence does not imply that they are not identified as
relevant within the existing literature. Instead, they are simply not the
primary focus of most papers in our dataset.

In the following, we elucidate three distinct levels of algorithmic impact
(individual, social, and biospheric) with examples stemming from the
literature and categorize the risks and harms emerging from our search using
this typology (see Table 1). This lens on algorithmic harm aims to account for
the intersectional dimensions and socio-technical embeddedness of foundation
models with the aim to better inform potential technical and normative
interventions to advance responsible AI.

###  4.1. Individual risks and harms

It is now undeniable that AI systems can have adverse impacts to individuals
which may be physical, psychological, or financial, but may also negatively
affect a person’s dignity, reputation, or fundamental rights and freedoms.
Indeed, individual-level risks and harms arising from the implementation of
data-intensive technologies have been largely documented in the literature,
with individual impacts having immediate as well as long-lasting implications
for those at the receiving end of such harms (Raymond, 2017). Equally so, a
focus on individual rights has been at the core of conceptions of data
protection, privacy, and security within digital regulation such as the
European Union’s General Data Protection Regulation (GDPR), where protection
and redress mechanisms privilege notions of personal data or data subjects
(Smuha, 2021).

Table 1. A mapping of foundation models risks and harms according to their level of impact (individual, social, biospheric). The table shows the number of papers across categories of harm and levels of impact, and the percentage (in brackets) of the total number of papers in the set in which the risk/harm appears. Papers appearing in more than one category were counted more than once. See Appendix for an explanation of each risk and harm category. Risks and Harms Category |  Individual |  Social |  Biospheric
---|---|---|---
Bias and societal prejudices |  71 (42.5%) |  71 (42.5%) |  0
(0%)
Misinformation, disinformation and propaganda |  45 (26.9%) |  45 (26.9%) |  1
(0.6%)
Unreliable performance |  43 (25.7%) |  40 (24%) |  0
(0%)
Cybersecurity risks and harms |  37 (22.2%) |  27 (16.2%) |  0
(0%)
Privacy risks and harms |  28 (16.8%) |  21 (12.6%) |  0
(0%)
Systemic social and economic risks and harms |  18 (10.8%) |  24 (14.4%) |  1
(0.6%)
Legal and regulatory violations |  17 (10.2%) |  17 (10.2%) |  0
(0%)
Environmental effects and ecological disruption |  8
(4.8%) |  8
(4.8%) |  19 (11.4%)
Misuse |  13 (7.8%) |  14 (8.4%) |  19 (11.4%)
Lock-in and opacity risks |  13 (7.8%) |  15
(9%) |  0
(0%)
Overdependency in human-computer interaction |  11 (6.6%) |  6
(3.6%) |  0
(0%)
Data risks and harms |  7
(4.2%) |  7
(4.2%) |  0
(0%)
Value misalignment |  2
(1.2%) |  2
(1.2%) |  0
(0%)
Extreme or catastrophic risks and harms |  2
(1.2%) |  2
(1.2%) |  1
(0.6%)

One of the biggest ethical concerns associated with AI systems has been their
role in creating discriminatory, biased, and unfair outcomes which impact an
individual’s safety, health, wellbeing, and integrity. The proliferation of
applications based on foundation models has not only augmented these concerns
but has given rise to a host of new ethical, privacy, and safety issues.
Biases and social prejudices present in training datasets and encoded in
foundation models trickle down to applications like chatbots or image
generators, thereby reproducing existing patterns of harm, exclusion, and
discrimination (Abid et al., 2021; Steed et al., 2022; Gadiraju et al., 2023).
For instance, chatbots—such as OpenAI’s ChatGPT or Google’s Bard—are capable
of producing natural language responses to prompts through complex statistical
associations (Bommasani et al., 2022; Zhao et al., 2023). These responses are
enabled by the vast amounts of data that the foundation models have been
trained on, comprising mostly of internet-scraped datasets that have been
shown to encode biased and harmful associations across protected categories of
religion, disability, gender, race, and ethnicity (Nadeem et al., 2020; Abid
et al., 2021; Deshpande et al., 2023), and also at the intersections of these
characteristics (Tan and Celis, 2019; Guo and Caliskan, 2021).

When it comes to issues of privacy and online safety, LLMs have been found to
be effective tools for fraudulent activities such as scams, impersonation, and
phishing, given their capability to generate personalized and highly
convincing messages at scale (Hazell, 2023; Derner and Batistič, 2023; Neupane
et al., 2023). Research has also shown that LLMs are prone to leaking private
or sensitive information contained within their training data corpus (Carlini
et al., 2023; Kandpal et al., 2022). Because these models are trained on data
scraped from the Internet, privacy violations might take place unbeknownst to
impacted individuals thereby limiting their ability to consent or opt out
(O’Neill and Connor, 2023). Furthermore, models can associate different pieces
of data to an individual, which poses privacy and security risks wherein
sensitive information or even wrongful associations are revealed without
consent (Huang et al., 2023a). Indeed, in some instances, chatbots have been
used with the intention of extracting protected information and bypassing
restrictions through the use of malicious methods like prompt hacking,
jailbreaking (Rao et al., 2023; Wang et al., 2024), and prompt injection
(Perez and Ribeiro, 2022; Greshake et al., 2023).

Lastly, the proliferation of foundation models complicates the already
significant negative impacts digital technologies have on people’s
psychological wellbeing. In the last decade, researchers have raised concerns
over how hyperconnectivity and unhealthy relationships with technology can
have serious mental health implications and influence how people form
individual identities and construct self-hood (Albrechtslund and Lauritsen,
2013; Lupton, 2016; Brubaker, 2020). These issues are now exacerbated with the
emergence of personalized AI chatbots capable of producing human-like
responses. New research into the use of these conversational agents for mental
health support warns about the potential for people’s overdependence on these
agents and the risk of social isolation (Ma et al., 2023). Similarly,
documented cases of users self-harming as a result of exposure to toxic
interactions with AI agents evidence the devastating impacts these
technologies can have on vulnerable individuals (Walker, 2023; Xiang, 2023;
Lee et al., 2023). For example, prior research has demonstrated that
individuals who are isolated may be more vulnerable to misleading information
on social media platforms (Bonsaksen et al., 2021). When individuals are
subject to algorithmic nudging and personalized information campaigns, they
are at risk of loss of autonomy, dignity, and integrity (Rosenberg, 2023).

###  4.2. Social risks and harms

While the rise of foundation models has generated huge expectations about
their positive transformational potential for society in many domains, there
are also concerns about how their adoption can contribute to worsening
existing social inequalities, biases, and injustices. Beyond individual risks
and harms, there are different ways in which technology can have wider
collective and societal impacts. One way to conceptualize collective risks and
harms is to look at the compounded or aggregate impacts to a particular group,
or to the functioning of a society as a whole (Malik et al., 2022).

Risks and harms to communities can be characterized by how particular
demographics and group formations within society are distinctively impacted by
technology, particularly when it comes to vulnerable groups with protected
characteristics, e.g., migrants, refugees, LGBTQI+ communities, Muslims, the
elderly. 333We note that notions of community are complex, often comprising
loosely held webs of relational experience and knowledge, which share geo-
spatial, identity-based, and/or digital spaces amongst other facets, with
community membership subjective and fluid (Bryer et al., 2020). Here,
communities are defined as groups sharing characteristics pertaining to
intersectionality, power, and positionality (Ruhland et al., 2023). As we have
shown, not only can these technologies produce responses that are
discriminatory and psychologically triggering for individual users, but the
reproduction of hegemonic views, harmful stereotypes, and biases can have
detrimental consequences at the collective level, affecting marginalized
groups and communities including those who are not direct users, or are far
removed from the technology. For instance, Abid et al. (Abid et al., 2021)
demonstrate the persistent religious bias present in foundation models, noting
that prompts containing the word “Muslim” produced responses with violent
language. These negative associations, in turn, can spur targeted hate and
disinformation campaigns centered around anti-Muslim or Islamophobic
sentiments.

Collective forms of harm can also be understood from a relational lens. The
relational lens captures the adverse impacts to social relationships of
interdependence, trust, and solidarity, thereby negatively impacting people’s
experiences of belonging, or their capacity to flourish through their
relationship to others. A rich and interdisciplinary body of research has shed
light on the range of interpersonal effects arising from people’s interactions
with algorithms, including the affective impacts of the algorithmic sorting of
content on social media feeds (Eslami et al., 2015; Bucher, 2017; Stark, 2018)
and the proliferation of misinformation as detrimental to the quality of the
public sphere and democratic deliberation (Bennett and Livingston, 2018;
Tsfati et al., 2020; Vaccari and Chadwick, 2020). Foundation models in
particular can increase the scale and speed at which disinformation campaigns
can be disseminated across the information ecosystem (Solaiman et al., 2019;
Pan et al., 2023). As generative AI applications powered by foundation models
flood the public sphere with fake information, there is a risk of eroding
public trust in the information that circulates online, further fueling social
polarization and the creation of echo chambers (Kirk et al., 2023; Simmons,
2023).

Lastly, algorithmic systems are inextricably embedded in wider social,
political, and institutional structures, and as such they also have a bearing
on those structures (Williams and Edge, 1996). To grapple with the impacts
that foundation models can have at a structural level, one must consider
geopolitical, socioeconomic, legal, and organizational manifestations of power
(Leslie and Rossi, 2023). For instance, some scholars have warned about the
tight concentration of power arising from the limited pool of actors
controlling technological capabilities globally (Crampton, 2019; Deibert and
Pauly, 2019; O’Hara et al., 2021); the influence of these power asymmetries on
the development of international policies, standards, and regulations for
technological practices (Baik, 2020; Chomanski, 2021; Cohen, 2019); and the
overwhelmingly invasive data practices carried out by governments and
companies (Eubanks, 2018; Fourcade and Gordon, 2020; Tréguer, 2019).

The collective and deeper structural effects of these technologies are
difficult to anticipate and measure as they manifest over time as AI becomes
more widespread and embedded in society. Nonetheless, it is helpful to analyze
and situate foundation models in the context of well-known social implications
linked with digital technologies. For instance, going back to the issues
related to the public sphere, one could argue that the effortless production
and spread of targeted misinformation and propaganda enabled by foundation
models, can have lasting effects on wider cultural, social, and political
structures, namely established democratic processes, representation,
participation in public life, and institutional trust. Given the evidence of
public and private actors deploying technologies to generate manipulative
content to sway public opinion (Funk et al., 2023; Collier et al., 2022),
scholars have warned about an increasing risk to social cohesion given the
mounting erosion of trust in democratic structures (Kreps and Kriner, 2023).

###  4.3. Biospheric risks and harms

Lastly, biospheric risks and harms refer to the potential adverse impacts to
ecological systems and environments, as well as wider impacts to the biosphere
at the planetary level (Potter, 1999). Importantly, this category also
includes harms to the individuals and communities negatively impacted by
environmental effects and ecological disruptions resulting from AI development
and use. In other words, biospheric, social, and individual risks and harms
are fundamentally intertwined. A good entry point to understand biospheric
impacts is to look at the largely hidden socio-material entanglements of data-
intensive systems. That is, the complex supply chains, labor, and underpinning
materials crucial to AI infrastructures—including the Rare Earth Elements
(REEs) that are necessary to manufacture the semiconductors essential to
computer hardware. The global nature of the supply chains of foundation models
pose individual, social, and environmental risks by entangling processes of
extraction, knowledge production, and ultimately decision-making (Rella,
2023).

The development of supercomputers which utilize REEs, and enable foundation
models to be trained, has been found to have significant material footprints
globally (Anton et al., 2020). The mining of silicon and other rare metals not
only cause environmental harms, but can rupture local communities (Crawford,
2021; Al Rawashdeh et al., 2016) and cause individual harm to the workers
mining these elements (White and Shine, 2016). Furthermore, the negative
consequences are usually borne disproportionately on vulnerable or
marginalized communities largely located in the global south (Bullard, 1993).
Thus, when put in the context of global infrastructures, the interrelated
nature of these risks and harms becomes clearer. Robbins and van Wynsberghe
argue that the “interconnectedness of AI with ecological, social, and economic
systems” (Robbins and van Wynsberghe, 2022) pose very real risks regarding
societal lock-in to AI infrastructures. That is to say that beyond a certain
point, the nature of the supply chains and materials powering certain AI
infrastructures cannot be changed, whilst ecological harms would continue to
multiply because of resultant carbon emissions, REE mining, and other
processes. Given these potential path dependencies, we are currently at a
crucial juncture for developing ethical governance mechanisms that address the
interlocking risks of the biospheric and human facets of AI ecosystems.

The development and implementation of data-intensive systems like foundation
models is also known to have high local environmental costs in the form of
energy consumption required to train increasingly large and complex machine
learning systems (Kirk et al., 2023; Luccioni et al., 2022), and heavy water
consumption needed for data center cooling (Li et al., 2023d). The amount of
compute needed to train large-scale models has doubled every 3.4 months since
2012 (Amodei et al., 2018; Hao, 2019), which translates to higher energy
expenditures, and the use of costly resources even if the improvements in
model accuracy are modest (i.e., diminishing returns in terms of model
accuracy come at increased computational cost, see (Schwartz et al., 2019)).
For example, the carbon emissions of training Google’s BERT were roughly those
of a transatlantic flight (Strubell et al., 2019). All of this at a time when
curbing our global emissions is crucial to slowing climate change down and
effectively mitigating its effects (United Nations, [n. d.]). Efforts to
develop responsible technologies that minimize their cascading impacts on the
environment are hindered by the difficulty in comprehensively assessing carbon
and energy footprints of training large models. Tools have emerged enabling
practitioners and developers of these energy intensive technologies to monitor
and track their models’ emissions (Anthony et al., 2020).

Although the environmental costs of AI are borne by everyone on the planet in
terms of negative externalities contributing to climate change and ecological
deterioration, they are not uniformly distributed among the world’s
populations or regions. The allocations of benefits and risks replicate
existing patterns of environmental injustice, coloniality, and “slow violence”
(Nixon, 2013), in which a disproportionate exposure to risks and harms is
borne by marginalized communities; be these in terms of pollution, destruction
of local ecosystems, or involuntary displacement. By contrast, the majority of
the economic benefits reaped by the use of the model mostly go to the model’s
proprietary owner(s), even when these technologies adopt an open source ethos
(Liesenfeld et al., 2023).

Finally, the impact of pollution and ecosystem degradation on interdependent
and connected biological systems (including sentient non-human animals) cannot
be ignored (Strubell et al., 2019; Coghlan and Parker, 2023). AI has the
potential to put non-humans at further risk (e.g., animals and the ecological
systems in which they live) through surveillance, monitoring, and testing for
which the benefits are largely directed to humans and society. Potential harms
arise from data collection on animals in intensive factory farming to increase
productivity, or data tracking of protected wildlife, which hunters and
poachers can hack to illegally hunt or trade animals (Bossert and Hagendorff,
2021; Coghlan and Parker, 2023). Beyond these physical harms to animals, the
functional use of AI in image recognition and as recommender systems
demonstrate biases in differentiating species borne out from underlying social
factors (Hagendorff et al., 2023). There is an urgent need to understand and
respond in a systemic way to the interconnected and shared vulnerabilities of
humans, animals, and the ecosystems in which we live and share (Coghlan and
Parker, 2023).

###  4.4. Summary of findings

Our review shows that a large proportion of papers (40%) raise concerns about
the potential for foundation models to perpetuate and amplify hegemonic views,
harmful stereotypes, and societal and behavioral biases on an unprecedented
scale. A similarly large set of papers (20%) also point to issues related to
the creation and spread of misinformation and propaganda as well as the
potential exploitation of these models for fraudulent services and
cybersecurity attacks by malicious actors. Furthermore, the individual and
societal impacts stemming from inconsistent or undesirable performance
constitute a significant part of the mapped academic literature, making the
case for cautious approaches to the deployment and use of such models.

Although less prevalent, a subset of the literature mapped point to concerns
in connection to a range of technical and supply chain challenges of
foundation models, and the potential compounding effects of those challenges
as foundation models become more widespread. These range from the reliance on
proprietary software and lack of transparency that enable lock-in and opacity
risks to issues like outcome homogenization, narrowing of the market or
monopolization, and the perpetuation of inequalities, which affect societal
and economic structures. Our mapping also reveals that approximately 10% of
the mapped papers address the environmental risks and harms associated with
foundation models. While the primary focus of these latter works is on the
biospheric-level impacts, some authors explore the interconnections between
the adverse impacts of foundation models on ecology and animals and their
consequential impacts on individuals and societies. This underscores the need
for expanded exploration and attention to holistically understand the
complexities surrounding the environmental adverse impacts of foundation
models. Lastly, despite the extensive emphasis in much of the governance
discourse, less attention is given in the academic literature to the extreme
scenarios of existential, catastrophic, and other speculative risks of
foundation models. These findings highlight the current visibility gap between
real-world consequences and speculative risks, and sheds light on the areas
requiring urgent and greater attention and efforts.

##  5\. Discussion: Grappling with the scale and interconnectedness of
foundation models

Foundation models are highly consequential technologies which have sparked
discussions about a spectrum of transformational impacts from the most
promising to the most concerning. They pose unprecedented governance
challenges but also offer an opportunity to draw lessons from, as well as re-
examine, the landscape of socio-technical impacts of data-intensive
technologies. While emergent and novel, foundation models do not arrive de
novo; they are “built on an installed base” (Star, 1999) and inherit the tools
and methods of prior generations of machine learning and neural network
technologies. As such, many of the foundation-model-related risks and harms
that we highlight are likely to appear familiar because they occur, to some
degree, in adjacent and precursor technologies.

Even so, two key differentiating characteristics of foundation models are
their massive scale and widespread embeddedness. Foundation models comprise
hundreds of billions of parameters, trained on mountains of data, that consume
enormous resources for both training and deployment. In particular, the scale
of foundation models means that the risks and harms they present are not only
likely to be magnified and amplified, but that this will happen in ways which
transcend national and political boundaries, requiring a multi-pronged and
transnational response. Harms that may have been minimal or just minimally
attended to in prior generations of technology are now made visible and
urgent. For example, data-intensive systems have been criticized for some time
for their carbon footprint (Stoll et al., 2019), but the high visibility and
widely publicized demands of foundation models have brought this issue to the
fore to the point that it is unsurprising to see this aspect discussed in the
technical literature (Bommasani et al., 2022).

Another differentiating characteristic of foundation models is their
embeddedness. Foundation models are conceptualized and architected as the base
models for many and diverse types of downstream applications. The embeddedness
of foundation models renders them invisible yet pervasive. As a result of
their platformized architecture, foundation models form the basis of many
thousands of extensions, and as such, the negative impacts and harms stemming
from foundation models may be obfuscated and rendered relatively intractable.
These two characteristics—scale and embeddedness—position foundation models to
be both highly adaptive, highly elusive, and highly dangerous. We argue that
any assessment of risks and harms should account for these socio-technical
interdependencies, and any design of mitigations and policy responses should
be commensurate with the level of impact be it individual, societal, or
biospheric. The value of our proposed framework for conceptualizing risks and
harms is that by decomposing these into individual, social, and biospheric
impacts, we provide a conceptual tool with which to challenge attempts to
narrow down salient risks and harms in a way that is meaningful only to some
discrete set of affected actors and thereby limited in effect. The prevailing
international policy discourse focusing on the technical safety of “frontier
AI” systems, which has led to a flight from confrontation of the full spectrum
of hazards presented here, is a signal example of such an erroneous narrowing
(Helfrich, 2024). In this paper, we underline the necessity of the opposite
approach. When identified at their empirical sources, the risks and harms of
foundational models become visible from all angles. They cut across
individual, collective, and environmental levels, spreading over and affecting
entire populations, including entire socio-material and biophysical ecologies
of humans and non-humans. In this way, the impact and importance of the full
range of risks and harms cannot be invisibilized or waved away in pursuit of
economic, geopolitical, or other short-term goals.

###  5.1. Visibility gaps in the current assessment of algorithmic impact

As we have shown in this paper, the literature on risks and harms of
foundation models is expansive and is likely to continue to grow. Grappling
with such a vast and heterogenous landscape is a challenging task both for
those who are attempting to build a nuanced understanding of the technology as
well as for those who are seeking to inform the debate on mitigations and
governance. We recognize notable attempts in the literature to taxonomize
these risks (Weidinger et al., 2022), as well as significant progress being
made internationally to reckon with the scale and embeddedness of foundation
models by framing AI governance as a human rights and transnational issue
(Intelligence, CAI; UNESCO, 2023). However, some of the most prominent
governance initiatives, particularly in Europe and the US, has thus far fallen
short of contending with the most problematic harms stemming from unequal
patterns of data, labor, and resource extraction and instead has focused
largely on risks to adoption, technical safety issues, and catastrophic risks.

One of the biggest challenges in assessing and anticipating algorithmic harm
has to do with limited evidence and difficulties related to observing the
indirect manifestations of harm, as well as foreseeing its effects over time.
Grasping these complexities involves nuanced and context-dependent
understanding. For instance, there are direct measurable impacts from the
energy used to train or operate an LLM which are immediate and visible, but
indirect harms arising from AI applications may only come to light over time
or when enough evidence and research is made available. An AI system enabling
the unsustainable extraction of mineral sites or unfair labor practices in the
data labelling supply chain, are just a couple of illustrations of how
negative indirect harms are likely to be largely hidden from view (OECD,
2022). Direct harms to individuals may translate to indirect harms to society
and vice versa. For instance, the potential harm that can ensue with data
misuse or privacy breach (e.g., personal data) is not just limited to the
individual who is directly manipulated, but indirectly affects the interests
of society at large (Smuha, 2021). Where anthropocentric and species biases
exist in AI, there are further examples of direct harm which remain
understudied, largely because wider biospherical needs are not part of the
conversation (Coghlan and Parker, 2023). Algorithmic systems that ignore
animals or privileges a particular view of animal welfare while ignoring
others can exacerbate these consequential impacts, and not least can lead to
indirect and direct harms to interconnected ecosystems shared by both humans
and animals (Bossert and Hagendorff, 2021; Coghlan and Parker, 2023).

###  5.2. Building a socio-technical scaffold to technical interventions

The current strategies for the mitigation of foundation model generated risks
and harms that have most traction among policymakers and governments
predominantly focus on technical interventions (OECD, 2023b). There is thus an
urgent necessity to delve into the social context within which these
interventions are situated. This is crucial, especially since the advent of
generative AI represents not only a significant milestone in technical
advancement but also simultaneously transforms the very fabric of far-reaching
socio-digital infrastructure like the Internet and its social experience
(Heikkilä, 2022; Heaven, 2022).

Consider, for instance, the issue commonly framed in technical terms as “model
collapse” or “data pollution”. In this scenario, the generative output from
the widespread experimental use of chatbots and image generators feeds into
public data pools. This influx poses the risk of influencing future datasets
that AI models will subsequently incorporate. The amalgamation of human and
AI-generated content potentially undermines the quality and diversity of AI-
generated outputs (Martínez et al., 2023). While this cycle undoubtedly
necessitates technical intervention, the risk typology outlined in this paper
enables the identification of concurrent impacts within the social
sphere—impacts like the undermining of the long-term integrity of the
information ecosystems on which modern democratic ways of life rest.

The integrative perspective we offer emphasizes, for example, that the risks
surrounding the AI-enabled generation of biased or harmful images go beyond
just offending individuals; in the aggregate, these systems have the potential
to change social narratives around communities and to lock in cultural
prejudices at scale, replicating and augmenting patterns of structural
discrimination and injustice. Likewise, given the scale of their generative
abilities, these technologies have broader planetary implications that derive
from the cumulative costs of mass industrialized compute. Concentration on the
perceived technical complexity of foundation models often mistakenly occludes
such a clear ecological view of the social impact of generative outputs.
Regardless, evolving research rightly insists that the AI community must grasp
the subtleties, social contexts, and boundaries of human interaction with AI
as a user-oriented technology, as well as the social and longitudinal aspects
of innovation, more broadly (Raji et al., 2022; Lehmann, 2023; Sanchez, 2023).
The risk mapping presented in this paper facilitates this understanding. Even
a singular, seemingly innocuous creation of politically incorrect content by
an AI tool, a feature that many such tools still permit (Hagendorff and Danks,
2023), can accumulate and result in a proliferation of societally effective
bias within the community. This bias is amplified by the repetitive
contributions from multiple individuals. Consequently, the framework presented
here helps us understand how concerns usually detected or faced at individual
levels, in fact, scaffold larger risks and contribute to higher-level
concerns, thereby providing a deeper socio-technical understanding of
foundation models and AI at large.

###  5.3. Toward an integrative perspective on risks and harms

Our framework illustrates concretely how claims that there may be gains from
the implementation and use of foundation models that outweigh their potential
and observed harms need to be examined and nuanced through sharpened socio-
technical lenses. Such claims often rely on a strictly utilitarian calculation
in which the overall potential “benefits” of foundation model application may
outweigh the total harms and risks and are hence largely incapable of
accounting for more systemic considerations. However, as shown by our
framework, foundation model harms are frequently difficult to track and
measure—and this affects the capacity to coherently weigh benefits and harms.
For instance, harmful use cases that are relatively intelligible when measured
individually could have impacts and consequences that are much harder to trace
at social and biospheric levels. A moderate, but cumulative and difficult-to-
perceive, harm to planetary health has implications for billions of people
whereas a significant, but straightforward, harm to some individuals may be
quite limited in scope. This makes utility calculations of benefits and harms
difficult to perform with sufficient precision to capture their full range,
scale, and scope. For example, Bommasani et al. (Bommasani et al., 2022) argue
that the benefits of releasing large models—such as applications that
translate text in otherwise underserved languages—outweigh the risks of misuse
and abuse by malicious actors. Within their analysis, there is also a
recognition that relatively few firms and organizations have sufficient
resources and capacities to produce foundation models and that efforts to
develop them for use by less-resourced non-elites is nascent and unlikely to
produce models with similar capacities. The analysis, however, fails to
perceive the deeper risks engendered by the asymmetrical power structures and
dynamics that lead to these inequitable differentials in resources,
capacities, and access.

Where a handful of highly self-interested and profit-driven companies control
the data, compute, and skills infrastructures on which the development and use
of foundation models rely, social harms arising from expanding inequality,
wealth polarization, concentration of economic power, and privilege biases
that lead to the escalating marginalization of minoritized groups will likely
abound. Risk analyses that fail to acknowledge macro-scale issues like this
will discount socially consequential adverse impacts. In this case, the very
fact that the architecture of foundation models relies on high resource
concentration should be a launching pad for expanding the narrow lens on
technical risk to account for the ways in which foundation models play a role
in reproducing harmful social hierarchies and planetary degradation. Such
recognition is also crucial for contending with the potential for foundation
models to lead to hard-to-reverse and long-term effects as this technology
progressively becomes more embedded in society. What we call an integrative
perspective is essential for overcoming the dominant utilitarian and
performance-oriented approach in the AI governance discourse which has tended
to frame societal challenges in terms of quantifiable trade-offs between risks
and benefits.

##  6\. Conclusion

In this paper we have confronted a blind spot in the evolving AI governance
landscape that derives from its reliance, particularly among prominent
policymakers in the global north, on speculative risks and selective seeing.
Drawing on the rapidly growing multidisciplinary body of research on
foundation-model-generated risks and harms, we have shown how discerning this
through integrative and socio-technically curved lenses better discloses the
full spectrum of impacts across individual, social, and biospheric levels. We
have argued, in this respect, that there exists a visibility gap between the
range of concerns and evidence raised in the critical, empirically anchored
literature, and the abstract and mainly hypothetical issues focused on within
some of the most influential international AI governance initiatives. Such a
gap reflects the convergence of the power dynamics, private interests, and
geopolitical priorities that have agenda-setting consequences in the AI
governance ecosystem—which is a challenge to coherently grappling with the
unprecedented industrialization of large scale data-driven technologies,
rapidly transforming veritably every domain of life and communities around the
world. While potentially beneficial and transformative, foundation models also
pose numerous risks to people, society, and the planet. We have aimed to
deepen understandings of this broad range of risks by bridging the technical
aspects of foundation models with their socio-technical underpinnings,
connecting individual concerns to collective and planetary issues, and doing
justice to the multifaceted and differential impacts these models have on
affected communities. Our conceptualization of risk, particularly regarding
the potential transformations effected by these technologies, demonstrates
that the visibility of risks and harms should not be concealed or obscured by
speculative concerns about existential threats, hypothetically conceived.
Instead, as under the framework presented here, an understanding of risks
should be grounded in robust evidence that is observable at various levels,
demonstrating the potential of these adverse impacts to escalate, over time,
and to widen in their scale and scope. Ultimately, the framework we have
presented enables a comprehensive assessment of algorithmic impacts for which
an interdisciplinary dialogue is key. As such it can be applied as an
analytical tool to inform socio-technical mitigations and to fundamentally
expand existing toolkits for algorithmic fairness, transparency, and
responsible AI.

## Impact Statement

1) Description of the ethical concerns the authors mitigated while conducting
the work (as part of an ethical considerations statement):
Throughout the process of developing the research questions and methodology,
we foregrounded our work in an acute awareness and recognition of the role
power plays in shaping conversation around the design, development, and
deployment of technology. We grounded the evaluation of risks and harms as
they relate to foundation models within well-established scholarship and
critical discussion surrounding how power is distributed, the pervasiveness of
stark power asymmetries, and how the differential impacts of foundation models
–and technology at large—are experienced across multiple actors and layers of
the ecosystem. We allowed this grounding to inform the framing of our research
questions and our methodology, ensuring that a multidisciplinary body of
scholarship was drawn on to inform our investigation. This approach included
drawing on critical data studies, science and technology studies, and
environmental justice scholarship, amongst other fields. It is through this
grounding that we aimed to challenge dominant discourses that adopt
speculative perspectives on the risks and harms presented by the rapid
deployment of foundation models; discourses which overwhelmingly distract from
current, real-world consequences. We therefore aim to foreground our work in
observed impacts. Through the proposed individual – social – biospheric
framework, we work to acknowledge the inherently entangled and interdependent
nature of socio-technical systems and their impacts, rather than further
dominant discourses of fragmentation and division.

2) Reflections on how their background and experiences inform or shape the
work (as part of a researcher positionality statement):
The final form and content of this work was shaped by a few factors. Firstly,
all researchers involved in the development and writing of this work are
academically trained in research, and are based at research and higher-
education institutions in the Global North, where the primary language is
English. As such, the research itself was limited to the English language. The
researchers involved in this work, however, represent a variety of communities
and come from a diverse range of backgrounds, including lived and research
experience in the Global South. Throughout the process of developing the work,
we continuously reflected on– and engaged with– how our own values, beliefs,
perspectives, and lived experiences inevitably shaped the work presented here.
The research team which developed this work is interdisciplinary, coming from
a variety of research specializations including anthropology, sociology, data
science, and information science, as well as diverse and global industry
experience.

3) Reflection on the adverse, unintended impact the work might have once
published (as part of an adverse impact statement):
In presenting this work, we are acutely aware that the research is squarely
grounded in a rapidly evolving field. As such, we acknowledge that the
proposed arguments and recommended pathways this research presents might not
be relevant in the near to distant future, and we do not prescribe any
solutions that could have an impact in the future. However, we also
acknowledge that –despite these intentions—our risks and harms codes, and the
subsequent categorizations we develop, may have unintended impacts; either
through mis-categorizing certain risks and harms, or not accounting for
others. These codes and categories were developed by our team’s collective
conceptualization of the terms ‘risk’ and ‘harm’; as we are each working
within our individual and group positionalities, there may be other forms of
risks and harms not fully captured in this research.

###### Acknowledgements.

The authors wish to thank Claudia Fischer and Janis Wong for their key
contributions to an earlier draft of this paper, as well as the three
anonymous reviewers and area chair for their very constructive feedback. This
work was supported by the Ecosystem Leadership Award under the EPSRC Grant
EP/X03870X/1, the Arts and Humanities Research Council Grant AH/Z505584/1, and
The Alan Turing Institute.

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