# Algorithms as Social-Ecological-Technological Systems: an Environmental
Justice Lens on Algorithmic Audits

Bogdana Rakova  Mozilla FoundationSan FranciscoUSA
<EMAIL_ADDRESS>and  Roel Dobbe  Delft University of
TechnologyDelftNetherlands<EMAIL_ADDRESS>
(2023)

###### Abstract.

This paper reframes algorithmic systems as intimately connected to and part of
social and ecological systems, and proposes a first-of-its-kind methodology
for environmental justice-oriented algorithmic audits. How do we consider
environmental and climate justice dimensions of the way algorithmic systems
are designed, developed, and deployed? These impacts are inherently emergent
and can only be understood and addressed at the level of relations between an
algorithmic system and the social (including institutional) and ecological
components of the broader ecosystem it operates in. As a result, we claim that
in absence of an integral ontology for algorithmic systems, we cannot do
justice to the emergent nature of broader environmental impacts of algorithmic
systems and their underlying computational infrastructure. Furthermore, an
integral lens provides many lessons from the history of environmental justice
that are of relevance in current day struggles for algorithmic justice. We
propose to define algorithmic systems as ontologically indistinct from Social-
Ecological-Technological Systems (SETS), framing emergent implications as
couplings between social, ecological, and technical components of the broader
fabric in which algorithms are integrated and operate. We draw upon prior work
on SETS analysis as well as emerging themes in the literature and practices of
Environmental Justice (EJ) to conceptualize and assess algorithmic impact. We
then offer three policy recommendations to help establish a SETS-based EJ
approach to algorithmic audits: (1) broaden the inputs and open-up the outputs
of an audit, (2) enable meaningful access to redress, and (3) guarantee a
place-based and relational approach to the process of evaluating impact. We
operationalize these as a qualitative framework of questions for a spectrum of
stakeholders. Doing so, this article aims to inspire stronger and more
frequent interactions across policymakers, researchers, practitioners, civil
society, and grassroots communities.

††copyright: acmcopyright††journalyear: 2023††copyright:
acmlicensed††conference: 2023 ACM Conference on Fairness, Accountability, and
Transparency; June 12–15, 2023; USA††booktitle: 2023 ACM Conference on
Fairness, Accountability, and Transparency (FAccT ’23), June 12–15, 2023,
Chicago, IL, USA††doi: 10.1145/3593013.3594014††isbn: 979-8-4007-0192-4/23/06

##  1\. Introduction

Recent critiques of the use of data and algorithmic systems have shown the
need for more comprehensive lenses that expand the analysis of impacts beyond
the datasets and algorithms, situating algorithmic audits in a social and
ecological context (Benjamin, 2019; Green and Viljoen, 2020; Dobbe et al.,
2021). The rapid rise in popularity of Artificial Intelligence (AI) has
received related critiques pointing at the lack of considerations of social
sciences and humanities (Sloane and Moss, 2019), and deficits to address
harmful outcomes of AI systems in the academic peer review processes (Hecht et
al., 2021). For the purposes of this paper, we use the terms AI, algorithms,
and AI systems interchangeably to refer to products or services that leverage
automated decision-making processes. Throughout these prior critiques, there
is a growing consensus to acknowledge the necessity of plurality in truths
while dealing with a shared resource or system. While these critiques have
recently emerged associated with the advent of new computing applications that
lean on data, high-dimensional compute, and algorithms, they are not new.
There have been earlier movements making comparable arguments, such as
investigating a critical technical practice to reform AI in the 90s (Agre,
1997; Malik and Malik, 2021), the informatics of domination (Haraway, 2006),
and alternative modes of heterogeneity in technology (Star, 1990).

From a Science and Technology Studies (STS) perspective, the social and
technical components of a system are inextricably connected and ontologically
non-distinct (Bijker, 1997; Jasanoff, 2015). Furthermore, scholars have
brought attention to the constitutive ways in which algorithmic harms emerge
from concrete choices made by algorithmic system developers, failing to
account for the dynamic nature of social relations within which technical
artifacts are situated (Green and Viljoen, 2020; Selbst et al., 2019). Through
a comparative study of the history of impact assessment and algorithmic
accountability, Metcalf et al. (2021) highlight that ”the impacts at the
center of algorithmic impact assessments are constructs that act as proxies
for the often conceptually distinct sociomaterial harms algorithmic systems
may produce.” Sociomaterial practices as in ”the constitutive entanglement of
the social and the material in everyday life” need to be understood in
relation to technology and the organizational and political structures that
produce particular technologies (Orlikowski, 2007). The disparate impacts and
struggles within the practice and materiality of everyday life have been at
the core of justice-focused studies of algorithmic harms (Noble, 2018; Hanna
et al., 2020; Ogbonnaya-Ogburu et al., 2020; Benjamin, 2020; Davis et al.,
2021). Social justice civil society groups have been on the forefront of these
investigations, and their research has been instrumental in formulating more
comprehensive accounts of algorithmic harm (Costanza-Chock, 2020; Buolamwini
and Gebru, 2018; Raji and Buolamwini, 2019).

In parallel, Environmental Justice (EJ) scholarship and grassroots communities
have been leading actors in addressing harms to marginalized communities and
the environment (Taylor, 2000; Mohai et al., 2009; Pellow, 2000). Their
struggles span over a longer time horizon and have led to effective strategies
for more comprehensive, integral, and inclusive actions in response to
environmental injustice. We leverage the evolution of interpretations,
debates, and practical methodologies that have been core to the EJ field
(Agyeman et al., 2003, 2016; Holifield, 2001), in broadening the abstraction
boundaries around socio-technical AI systems. Given the more recent advent of
algorithmic harms and the relative early phase of algorithmic justice as a
field, we claim that there are many lessons that can carry over from the more
mature EJ field. They could inform the lifecycle of algorithmic impact
assessments while also contributing to building effective movements that
challenge algorithmic harms and promote algorithmic justice.

Recent work has engaged with the intersection of algorithmic systems and
environmental sustainability (Dobbe and Whittaker, 2019; van Wynsberghe, 2021;
Rohde et al., 2021) highlighting the lack of sufficient transparency into the
energy, carbon, and broader environmental footprint of digital infrastructures
and the tendency of data-heavy artificial intelligence techniques to
exacerbate these, often in ways that reify existing injustices. However, to
the best of our awareness, there is lack of studies investigating the
implications of key EJ concerns within the AI impact assessment process. To
fill this gap, we take inspiration from how the EJ field has approached the
complexity of technologies and their social and ecological impact. Central in
this approach is a system lens that brings into view the couplings and
emergent dynamics between technological, social, and ecological elements of
the broader system in which the technology resides (Leach et al., 2010). In
what follows, we introduce such an approach to allow scholars, engineers,
policy-makers, civil society organizers, and other diverse stakeholders to
address these emergent challenges through understanding algorithmic systems as
ontologically indistinct from Social-Ecological-Technological Systems (SETS).
As such, a SETS approach becomes a foresight tool that enables us to expand
the assumed boundaries of algorithmic systems and question their broader
implications in a more-than-human world. Through bringing in perspectives from
scholars studying social-ecological and social-ecological-technical systems
(Ahlborg et al., 2019; Biggs et al., 2021; Leach et al., 2010), we aim to
empower diverse stakeholders involved in the design and practice of
algorithmic audits, to unpack and address a broad range of concerns.

The remainder of this paper is organized as follows: in Section 2 we draw
attention to emergent challenges in the algorithmic auditing ecosystem. We
motivate a qualitative questions framework that integrates considerations from
SETS and EJ, which we detail in Sections 3 and 4, respectively. In conclusion,
in Section 5 we offer a starting point for diverse practitioners to situate
themselves within the thematic challenges emerging from the field of EJ and
work towards new practices for evaluating and overcoming harm and injustice in
algorithmic social-ecological-technological systems.

##  2\. Emergent challenges in the algorithmic auditing ecosystem

A growing body of work across interdisciplinary fields has engaged with
algorithmic auditing and impact assessment as a mechanism for algorithmic
evaluation and accountability. In a recent field scan, Costanza-Chock et al.
(2022) conduct an anonymous survey among 189 individuals who lead algorithmic
audits or whose work is directly relevant to algorithmic audits, identifying
that their biggest challenges are lack of buy-in for conducting an audit and
lack of enforcement capabilities. They also find that predominantly
practitioners in the algorithmic auditing ecosystem rely on quantitative tools
instead of qualitative methods, leveraging ”quantitative ‘definitions’ of
fairness to assess whether or not the distribution of goods and services
across various subpopulations are equitable or fair” (Costanza-Chock et al.,
2022). This is a challenge that Davis et al. (2021) frame as _algorithmic
idealism_ , ”enacting computation that assumes a meritocratic society and
seeks to neutralize demographic disparities.” A dislocated sense of
accountability (Widder and Nafus, 2022) and the lack of an accountability
forum (Metcalf et al., 2021) contribute to tensions when practitioners engage
in operationalizing algorithmic auditing frameworks in practice (Raji et al.,
2020; Rakova et al., 2021).

Another trend concerns growing critiques of the efficacy of audits and
documentation-based practices for promoting safer and more equitable
algorithmic systems. Cath and Jansen (2021) argue that recent enthusiasm
around algorithm registers in Europe deserves further scrutiny, as their
empirical insights from digital welfare contexts show that the register does
not situate algorithms in a sufficiently rich social and political context to
properly understand its true social impact. Gansky and McDonald (2022)
demonstrate that an emphasis on documenting and reflecting on contextual
factors in the design and assessment of algorithmic systems abstracts away its
reliance on institutions and structures of justice. They emphasize how justice
infrastructures are often under-equipped to manage disputes, and how the
current political economy of AI implementation, prioritizing profit seeking
over duties of care, further obstructs realizing rights.

One crucial part of this political economy is laid out in detail by Whittaker,
who explains how modern AI fundamentally depends on corporate data, compute
resources, and business practices (Whittaker, 2021). She warns about the
growing reliance on AI in industry, consolidating power over our lives and
institutions, while also using their financial and geopolitical power to
determine much of what is possible to counter their power in terms of
research, policy making, civil advocacy, and activism. These politics have
heavily shaped the discourse within AI research communities (Birhane et al.,
2022), and are increasingly encroaching on research addressing the social and
political impact of AI (Young et al., 2022), thereby preventing an honest and
inclusive account of algorithmic harms and their political dimensions.

The same concentration of power and resources has been constitutive to another
unavoidable trend: the ability to use computational resources to build large
machine learning models. In recent years, awareness has grown about the
environmental impact of such algorithmic systems and their underlying
computational infrastructures. Strubell et al. (2019) estimate the
environmental costs of Large Language Models (LLMs), while Lottick et al.
(2019) offer 14 policy recommendations on how to reduce it. Initiatives like
Green AI (Schwartz et al., 2020), Green Deep Learning (Xu et al., 2021), and
online tools like MLCO2 calculator111https://mlco2.github.io/,
CodeCarbon222https://codecarbon.io/, and
CarbonTracker333https://carbontracker.org/ aim to shift existing norms in AI
development by advocating for the reporting of carbon footprint alongside
accuracy and other efficiency metrics in AI. Increasingly, scholars have
worked on estimating the carbon footprint of LLMs, spanning up to hundreds of
billions of parameters and requiring millions of computing hours to train
(Strubell et al., 2019; Luccioni et al., 2022). Carbon footprint is only one
dimension of environmental impact and in particular the couplings between
environmental ecosystems and technical infrastructure (Dobbe and Whittaker,
2019), which we frame as ecological-technological couplings in the next
section. Other aspects of ecological-technological couplings issues, for
example, water pollution due to cloud compute companies’ operations (Anson et
al., 2022), have received much less attention.

Next, we point to social-technological couplings between LLMs and social
norms, behaviors, and value systems which evolve as people interact with the
technology. Within this aspect of downstream AI impacts, Bender et al. (2021)
call for the consideration of how risks and benefits of LLMs are distributed,
leveraging the literature on environmental justice and environmental racism
(Westra and Lawson, 2001; Bullard, 1993). Furthermore Bietti and Vatanparast
(2019) discuss how cloud computing companies, and Amazon in particular,
contribute to indirect environmental harms through contracts with oil and gas
industry as well as donations to political candidates denying climate change.

Lastly, another dimension of LLMs’ downstream impact we need to consider is
related to social-ecological couplings. At its core, the field of social-
ecological systems research engages with interactions between people and
nature (Biggs et al., 2021). Scholars in this field ask questions related to
knowledge co-creation and human decision-making which will inevitably be
disrupted by the wide-scale use of LLMs in society. In sum, we aim to build on
prior work arguing that increasing carbon emissions are undeniably coupled
with algorithmic systems and deserve urgent attention. Furthermore, resisting
the approach to equate environmental impact of AI to carbon footprint, we turn
to prior work in the field of EJ.

Justice frameworks have been central to a growing number of investigations of
algorithmic harms (Sloane, 2019; Costanza-Chock, 2020; Eubanks, 2018; Noble,
2018). We see the potential to further expand the bounds of algorithmic
justice to both learn from and also take into account EJ concerns and benefit
from its longer history. In this paper, we conceptualize EJ through a broad
frame that includes theoretical scholarship, policy, and practice. The
evolution of methodologies and multiple interpretations of EJ have contributed
to emergent themes within the EJ frame and discourse, including: (1) focus on
the practice and materiality of everyday life; (2) the impact of community,
identity, and attachment; and (3) growing interest in the relationship between
human and non-human assemblages (Agyeman et al., 2016). In what follows, we
show how EJ scholars have grappled with these issues and their relevance to
calls for algorithmic justice. We aim to highlight and uplift non-Western,
decolonial, and intersectional voices in how broadening the conception of the
environment and EJ (Wald et al., 2019; Png, 2022) could positively contribute
to a just and equitable algorithmic auditing process in a more-than-human
world.

Algorithmic systems don’t exist in a vacuum but are technical infrastructure
embedded in a social and ecological context (Leach et al., 2010). Ahlborg et
al. (2019) provide an in-depth review of prior literature, interdisciplinary
research, and empirical work, to show that “excluding technology or
environment from the problem formulation, conceptual framework, and the
analysis [methodological choices] precludes conclusions or policy
recommendations that engage these at anything but a superficial level.” For
example, one of the challenges they highlight is that socio-technical and
socio-ecological systems scholars have differing approaches to how they define
system boundaries and choose temporal, spatial, and quantitative scales of
observation and measurement frameworks. Ahlborg and Nightingale (2012) have
illustrated this through empirical examples in Nepalese forest management,
using a mixed-methods approach in revealing the impact of disparate scales of
knowledge. They highlight that “there are multiple positions within local
knowledge systems and how these positions emerge through people’s use of and
relations to the forest, in a dynamic interaction between the natural
environment and relations of power such as gender, literacy, and caste.”
Furthermore, algorithmic systems used in forest ecosystem management have
predominantly relied on a different scale of observations - satellite imagery
and sensor data - which environmental scientists have argued is introducing
temporal and spatial biases which haven’t been thoroughly investigated
(McGovern et al., 2021; Rolnick et al., 2022). Without a broader and more
integral lens addressing the couplings of algorithmic systems with socio-
ecological and socio-technical systems, the algorithmic impact assessment
process is bound to miss how technical interventions threaten to exacerbate
existing power imbalances and yield harmful consequences. Therefore, in the
next section we detail an integral analysis framework and its relevance to the
algorithmic auditing process.

##  3\. Introducing Social-Ecological-Technological Systems analysis

In order to properly understand how outcomes of a _system_ relying on AI and
algorithmic technologies come about, we benefit from questioning and
illuminating the elements that construct and constitute that system. A complex
dynamical systems lens is central to most sustainability studies. Here, we
leverage the definition of a system by Leach et al. (2010) \- a system is ”a
particular configuration of dynamic interacting social, technological, and
environmental elements.” The authors argue that there’s a need to understand
systems in terms of their structures and functions - _what are their
boundaries?; how are interactions across these boundaries constructed and
constituted?; what are their functional outputs and their consecutive
impacts?_ By drawing from French philosopher Michael Foucault’s theories
centered on the relationship between power and knowledge (Foucault, 1991),
they highlight the role of _constitution_. To understand constitutive
relations in a system, we must understand: (a) the role of power dynamics
among actors who determine the system boundaries, (b) the system’s internal
dynamics, and (c) interactions with what’s external to the system boundaries.

A central approach to studying complex dynamical systems and their
constitutive relations is Social-Ecological-Technological Systems (SETS)
analysis. The SETS approach is centered on investigating the couplings between
social, ecological, and technological dimensions of a system which are further
defined as the social-behavioral, ecological-biophysical, and technological-
infrastructure domains of the system (Pineda-Pinto et al., 2021; McPhearson et
al., 2021; Markolf et al., 2018). These domains are interdependent and
interact with each other through three different relationships or couplings.
McPhearson et al. (2021) define these relationships as follows (see also Table
1):

> “(1) social–ecological couplings refer to human–nature or social–ecological
> relationships, feedbacks, and interactions, such as how urban nature
> provides ecosystem services to support human health and wellbeing or
> linkages between stewardship of urban green spaces and ecosystem change; (2)
> social–technological couplings refer to the ways in which technology and
> human social systems interact such as providing ability to communicate
> globally through social media or the dependence on technological
> infrastructure to facilitate dense human living in cities; and (3)
> ecological–technological couplings refer to the different ways in which
> climate and biophysical systems impact technology [and vice-versa] such as
> wildfires which cause power outages or rising temperatures driving increased
> energy use for cooling technology in buildings which in turn contributes to
> the urban heat island.” (McPhearson et al., 2021)

The SETS couplings framework has emerged through the works of scholars from
the fields of Ecosystem Studies, Environmental Studies, Sustainability
Science, Urban Ecology, and others. Ahlborg et al. (2019) provide a
comprehensive review of prior literature in these fields, synthesizing four
arguments for the development of SETS analysis - (1) the need to better
understand technological mediation of human–environment relationships, (2) the
lack of a critical lens on the ambivalence of technology in respect to its
impact, (3) the need to assess how agency and power are mediated through
technology, and (4) the complexity of dynamics of change across scales of
human impact on environmental ecosystems.

Table 1. Social-ecological-technological systems couplings (Pineda-Pinto et al., 2021; McPhearson et al., 2021; Markolf et al., 2018) |  Social-Ecological Couplings |  Ecological-Technological Couplings |  Social-Technological Coupling
---|---|---|---
Key considerations |  _community-nature interactions shape social values, attitudes, behaviours, decision-making, and socio-ecological memory; in turn nature changes, reacts, and adapts_ |  _extractive and polluting technological-infrastructures; responsive hybrid green-grey, circular economy, and industrial ecology approaches to restore ecological processes_ |  _social norms, behaviors, values and belief systems create, maintain and replace technological-infrastructure systems; value systems shape and evolve as the interact with technological systems_
Table 2. Environmental justice dimensions and SETS: integrating the frameworks in the context of algorithmic auditing EJ Dimension | Social-ecological-technological couplings
---|---
|  Social-Ecological |  Ecological-Technological |  Social-Technological
Practice and materiality |  _Does the AI system, including its potential downstream derivative applications, impact the way people interact with ecosystem services, for example, access to water, food, and energy? Does access to the AI or the resources it depends on, i.e. internet connectivity, lead to unfair outcomes? How does the AI project interfere with material resource flow and in what ways would that burden vulnerable social groups?_ |  _What are potential impacts, risks, and threats of the material flows and ecological dependencies of AI systems and their computational infrastructure along the lifecycle - from mineral extractivism, to data centers and infrastructures, to electronic waste dumps? How do we measure and provide sufficient transparency over the carbon and ecological costs of computational infrastructure?_ |  _How does the process of design, development, deployment, and auditing of an AI system interfere in the politics of sustainability of everyday life? For example, how might data collection, labeling, and use practices interfere with the way people provide for basic human needs, i.e. access to water, food, and energy? What resources and avenues exist to meaningfully investigate or regulate the AI system and its computational infrastructure?_
Community, identity, and attachment |  _How might the AI system interfere with situated knowledges of local communities as well as their sense of collective identity and attachment to particular territories? How might the AI system interfere with displacement due to environmental disasters?_ |  _How do material / ecological impacts of AI and computational infrastructure vary across spaces and communities? To what extent are such impacts made visible as part of the impact assessment process? What data is made visible to whom? How is data about the broader computational infrastructure made visible?_ |  _What is considered data in building and auditing the AI system? Whose knowledges are taken into account? How does the AI system impact (un)intended users’ individual and collective sense of identity as well as identity transitions? And what agency and power is given to engaged communities and stakeholders to shape the audit or design?_
Human and non-human assemblages |  _Could we consult with environmental ethicists, environmental scientists, environmental lawyers, or grassroots organizations who could provide a critical lens for how the AI project interferes with human-nature or community-nature interactions?_ |  _What are the risks of the AI system’s computational infrastructure to land use, deforestation, biodiversity, habitat destruction, over-exploitation, and other aspects of environmental degradation?_ |  _How might algorithmic bias arise outside of human populations? How might the AI system impact local social relationships to the more-than-human world within the communities where it is deployed?_

By bringing in interdisciplinary prior work by scholars investigating socio-
technical and socio-ecological systems, here, we motivate the use of a SETS
approach to better understand the environmental dimensions of an algorithmic
audit. We propose a set of questions that aim to help practitioners
operationalize a SETS approach to the algorithmic audit. The questions we lay
out in Table 2 serve as a starting point to enable further interdisciplinary
dialogue and empirical investigations during an AI audit. We seek to make our
analysis actionable for diverse stakeholders who would be best positioned to
explore the EJ dimensions of algorithmic SETS impacts. The goal of the
framework is to propose a SETS-based EJ approach to algorithmic audits, which
we motivate through learning from the way the EJ community has grappled with
issues similar to those within the algorithmic justice field. The questions
along the dimensions of Table 2 aim to integrate (1) the structural dimensions
of a SETS analysis and (2) the emerging themes within EJ practice and
scholarship. In the rest of this section, we outline key considerations and
challenges in the social–ecological, ecological–technological, and
social–technological couplings of a SETS analysis. Specifically, we focus our
discussion on aspects which have not received much attention in the context of
algorithmic systems and provide pointers to more literature for interested
practitioners to further engage with.

###  3.1. Social-ecological couplings

The research within sustainability and sustainability transitions has been
broadly influenced by the study of social and ecological systems linkages with
regards to the complex interrelationships across human actors, natural
ecosystems, and the role of institutions in decision-making and governance
(Clark and Harley, 2020). Ostrom (2007) proposes a foundational Social-
Ecological Systems (SESs) framework which investigates interactions between
”(i) a resource system (e.g., fishery, lake, grazing area), (ii) the resource
units generated by that system (e.g., fish, water, fodder), (iii) the users of
that system, and (iv) the governance system.” In particular, they point to the
importance of analysis methodologies that establish a common vocabulary for
the comparison of specific empirical settings. In their early work, SES
scholars point to the use of SES frameworks and methodologies in investigating
the questions of governance of technological systems such as power grids or
telecommunications systems (McGinnis and Ostrom, 2014). Furthermore, the SES
body of research and practice has also developed a deep understanding of harms
through the lens of vulnerability analysis (Turner et al., 2003; Berrouet et
al., 2018), which has emerged through the body of work on environmental risks
and hazards (Cutter, 2002; Tobin, 1997). Building on critical reflections on
the ontological grounding of SES scholarship and drawing from the field of EJ,
Pineda-Pinto et al. (2021) point out that a number of recent SETS studies have
“only partially represented socio-ecological system interactions and often
neglecting the technological infrastructure subsystems.” To remedy that, they
bring perspectives from the field of EJ theory and practice to propose and
empirically test an evaluation framework grounded in qualitative questions
that inform the selection of SETS measurement indicators (Pineda-Pinto et al.,
2021).

###  3.2. Ecological-technological couplings

SETS scholars have argued that technological-infrastructure systems mainly
”extract resources, pollute and destroy in their sources, through their flows,
and in the places in which they end up as waste” (Pineda-Pinto et al., 2021),
calling for the need to investigate ecological-technological couplings. A
number of studies have been done investigating these interrelations in the
context of the built environment (Anderies, 2014; McPhearson et al., 2016;
Rozzi et al., 2015). Furthermore, a number of scholars in the socio-technical
research domain have engaged in SES analysis in evolving governance frameworks
that encompass technical infrastructure (Foxon et al., 2009; Smith and
Stirling, 2010).

In the context of AI, Matus and Veale (2022) argue that measuring algorithmic
impact should build on analysis from the sustainability domain, specifically
”the study of socioenvironmental issues and sustainability more broadly in
commodities and supply chains.” Learning from sustainability, they propose the
use of a robust certification system, including: (1) a set of technical
standards which reflects a theory of change, (2) a certification process which
facilitates monitoring and enforcement, and (3) a labeling program to provide
information to consumers. One of the major challenges they discuss is that
existing measurement frameworks are driven by network effects, making
consensus among diverse stakeholders an intrinsic rather than instrumental
goal.

Furthermore, we identify a challenge in terms of the ability of existing
measurement frameworks to assess downstreams impacts of the use of an
algorithmic system in a particular local context (Clutton-Brock et al., 2021;
Musikanski et al., 2018). While we have seen a growing number of scholarship
with regards to measuring the carbon footprint of algorithmic systems, there
has been less attention given to other environmental sustainability concerns
such as land use, water pollution, deforestation, biodiversity, habitat
destruction, over-exploitation of resources, and others (Dryer, 2020).

###  3.3. Social-technological couplings

At the core of the socio-technological couplings within a SETS approach are
considerations related to the interactions between social and technical
systems and their interrelationships with evolving social norms, behaviors,
values, and belief systems. Many recent studies have pointed to the importance
of understanding algorithmic systems as socio-technical systems i.e. a
combination of social and technical components interacting in complex dynamic
ways. By drawing on studies of socio-technical systems in STS, Selbst et al.
(2019) introduce a critical framework that informs the need to contextualize
machine learning systems in a way that expands their boundaries to include
social actors rather than purely technical ones. Furthermore, a growing number
of STS scholars have argued for the need to reconceptualize algorithmic harms
within a theoretical model of justice. Sloane (2019) argues that there’s a
need to reconfigure algorithmic discrimination as an issue of social
inequality by centering social justice in technology policy and computer
science education. Similarly, in examining the freedom and justice dimensions
of AI, Coeckelbergh (2021) suggests that “institutionally, we also need more
permanent interfaces between, on the one hand, technology development (in
industry, academia, etc.) and, on the other hand, political and societal
discussions.” We see that a justice oriented STS approach to addressing the
impact of an algorithmic system through the lens of socio-technological
couplings has already been adopted by a number of practitioners. However, it
needs to be mentioned that addressing the political dimensions of
AI/algorithmic systems, whether in academia, among workers, policy makers or
civil society, is challenged by the fact that most financial resources as well
as the AI technologies and infrastructures themselves are increasingly
controlled by a small number of tech companies (Whittaker, 2021). This forms
an inevitable socio-technical coupling and barrier that requires broader forms
of organizing to overcome, and which we hope can be inspired by traditions in
the EJ movement and related thinking promoted in this manuscript.

##  4\. Mirroring Algorithmic Justice to Environmental Justice concerns

The explored couplings necessary to understand algorithmic harm and injustice
motivate the use of a SETS approach. However, it would be inefficient to apply
the approach blindly, as the question remains what _model of justice_ to
embrace to orient ourselves to the salient harms and vulnerabilities that need
to be unpacked. Here we conceptualize a model of justice through lessons
learned within the field of Environmental Justice (EJ). Theory and practice in
the EJ movement has long grappled with struggles related to (1) distributive
justice - equity in the distribution of environmental risk; (2) recognitional
justice - recognition of the diversity of participants and experiences in
affected communities; and (3) procedural justice - opportunities for
participation in the political processes that create and manage environmental
policy (Holifield, 2001). As such, its legacy serves as a model of justice,
both in its normative orientation as well as in its evolution as a field and a
movement. In the most recent EJ annual review, Agyeman et al. (2016) provide a
comprehensive overview of EJ literature and goals in advancing climate
solutions that link human rights and development in a human-centered approach,
placing the needs, voices, and leadership of those who are most impacted at
the center.

###  4.1. Practice and materiality

This theme within EJ encompasses (1) the politics of sustainability of
everyday life, in the way we provide for basic human needs, for example,
access to water, food, and energy, and (2) the unsustainable material flows
that contribute to inequalities with regards to the access, security, and
sovereignty to such resources on local, national, and global scales (Agyeman
et al., 2016). EJ concerns here are deeply intertwined with the issues of
toxic waste, health disparities, food justice, indigenous justice, and climate
justice. The focus on the materiality of everyday life also emphasizes the
embodied experiences of environmental injustices by the human bodies of women,
children, people of color, indigenous people, and marginalized communities.
From an EJ perspective, “the material life of the body is seen as fully
entangled with questions of social justice, providing a stronger foundation
for an intersectional politics of alliance building between environmental and
EJ movements; between the global north and south; and across the racial,
cultural, sexual, and gender boundaries that mark bodies” (Agyeman et al.,
2016).

In bringing awareness to the material flows in the lifecycle of an algorithmic
system, practitioners could learn from the work of a growing number of
communities at the forefront of the EJ movement. It is vital to learn about
and acknowledge the historical, cultural, and sociopolitical context out of
which specific grassroot communities have emerged and seek to understand if
there could be better alignment and reciprocal relationships between (1) EJ
efforts related to the practice and materiality of everyday life and (2) the
critical work on investigating how material flows are impacted by algorithmic
systems. Exploitative and extractive data collection and use practices have
been central actors within emerging theories of data justice, data capitalism,
data colonialism, and data sovereignty, informing theories of algorithmic
reparation, algorithmic coloniality, algorithms of oppression, and others
(Thatcher et al., 2016; Kukutai and Taylor, 2016; Mohamed et al., 2020; Noble,
2018; Davis et al., 2021). Similarly to emerging trends in EJ, STS scholars
have taken an intersectional approach to illuminating the power dynamics,
structural conditions, and socio-technical context within which algorithmic
systems exist (Hoffmann, 2019; Mhlambi, 2020; Katell et al., 2020).

Thus, an EJ approach to the algorithmic impact assessment process would
involve the consideration of material resource flows along the entire
lifecycle of an algorithmic system including the supply chains of the digital
infrastructure on which it runs (Pasquinelli and Joler, 2020; Widder and
Nafus, 2022). Learning from and building meaningful relationships with
existing civil society actors, grassroots movements, and local communities,
there’s a need to understand how algorithmic systems disrupt these flows
(Ganesh and Moss, 2022; Hasinoff and Schneider, 2022), leading to higher order
impacts to the livelihoods of people and the resiliency of environmental
ecosystems. This is not an obvious and mechanical exercise and requires
building respectful relationships that enable shared vocabulary. We highlight
the need for the AI audit process to embrace a continuous self-reflexive
practice, acknowledging the positionality of different actors involved, and
how it shapes power imbalances and challenges towards mutual understanding
reciprocity (see also Table 2).

###  4.2. Community, identity, and attachment

The main argument in this body of EJ practice and scholarship is that
environmental injustices have spatial and cultural dimensions. Major issues
highlighted by Agyeman et al. (2016) are related to the interdependencies
between EJ and identity, community, space, place, and agency, linking to
critiquing the problems of gentrification, displacement, and homelessnes.
Furthermore, from the perspective of EJ, sustainability is seen as context-
specific and relational. We find that a large number of prior work on
algorithmic auditing and algorithmic harms investigations fall within this
thematic area of EJ concerns (Noble, 2018; Benjamin, 2019). Building on that
we aim to highlight emerging learnings and synergies.

From an EJ perspective, it is important to consider the way individual and
collective identity is deeply connected to people’s attachment and connection
to place (Anguelovski, 2014). While scholars have recently exposed how
identity is shaped by algorithmic systems (Hanna et al., 2020; Stark, 2019;
Keyes et al., 2021), we see a need to further disentangle the relationships
between identity, place, attachment, and algorithmic systems. Individual and
collective identities are dynamic and mutable - they change and evolve over
time and that process allows us to develop a more coherent sense of self.
Diverse scholars have shown how algorithmic systems interfere with identity
transitions - our ability to adapt to different life situations, such as
gender transitions, divorce, career change, and others (Haimson and Hoffmann,
2016; Haimson et al., 2015). Displacement due to environmental disasters
inevitably leads to identity transitions for impacted communities and entire
populations who might turn to AI-enabled products and services in crisis
situations. While there have been analyses of the use of AI in the field of
disaster risk management (Moitra et al., 2022; Kuglitsch et al., 2022), we
point to the need to more thoroughly account for a broad number of concerns
emerging within EJ and algorithmic justice critiques. In sum, algorithmic
audits need to engage with EJ-related data and situated knowledges within the
communities where a particular AI technology is deployed (Kaack et al., 2022).
Tracing the links between historical, representational, measurement, and
deployment biases, needs to be grounded in and informed by the lived
experiences of people impacted by the technology in a particular place.

###  4.3. Human and non-human assemblages

Finally, we highlight that an emerging theme within EJ discourses brings
attention to the relationship between human and non-human sustainability. Key
issues discussed by Agyeman et al. (2016) encompass the environmental
degradation of ecosystems, leading to increased vulnerabilities for their
inhabitants. Fundamentally, EJ adopts a pluralist approach to the relationship
between environment and justice - “an internal pluralism, incorporates a
diversity of necessary forms of justice, rather than privileging only one, and
retains flexibility in how functionings and flourishings are to be secured”
(Walker, 2009).

EJ asks us to learn from the body of work in Ecolinguistics which is situated
at the intersection of the fields of Human Ecology and Linguistics. It
investigates _the stories we live by_ , postulating that stories are
linguistic devices that we could analyze to judge their impact (Stibbe, 2015).
Within the view of AI as an instrument of knowledge extractivism (Pasquinelli
and Joler, 2020) or AI as an enabler for respectful and reciprocal non-human
kin (Lewis et al., 2018), or other framings, we argue that a self-reflexive
approach to the use of metaphors could enable improved outcomes in engaging
with the social and environmental impacts of algorithmic systems. In alignment
with prior work by STS scholars, we question the metaphors used in AI. In
their literature review, Stark and Hoffmann (2019) demonstrate that “the
metaphors we deploy to make sense of new tools and technologies serve the dual
purpose of highlighting the novel by reference to familiar, while also
obscuring or abstracting away from some features of a given technology or
practice.” For example, Seaver (2019) conceptualizes recommender systems as
_traps_ , arguing that:

> ”returning to the time structure of trapping makes the continuity between
> traps and [recommender system] infrastructures more visible: an
> infrastructure is a trap in slow motion. Slowed down and spread out, we can
> see how traps are not just devices of momentary violence, but agents of
> ‘environmentalization’, making worlds for the entities they trap.” (Seaver,
> 2019)

Furthermore, diverse scholars have questioned who is the human in human-
centered algorithmic systems (Chancellor et al., 2019; Aragon et al., 2016)
calling for the account for non-human living beings (Umbrello, 2021; Ziesche,
2021; HG Solomon and Baio, 2020). Building on their analysis we take
inspiration from the field of EJ to engage practitioners in evolving
algorithmic impact assessment frameworks that embrace a pluralist approach to
considering the non-human in the co-constitution of algorithmic impact. In the
next section we explore this further by leveraging a critical SETS approach to
the algorithmic auditing process.

##  5\. Discussion and Policy Recommendations

Adopting a complex SETS approach to the algorithmic auditing process helps in
illuminating the systemic complexity of justice concerns, which allows for
more effective normative debate and design of interventions. Algorithmic
systems are ontollogically indistinct from SETS, that is: (1) they are
dependent on social and ecological resources to be built, used, and
maintained; (2) they contribute to carbon and ecological costs through their
dependency on computational infrastructure and their role in fossil fuel
production; and (3) any problem that is solved through AI has underlying
technical, social, and institutional components and their interactions and
infrastructures depend on and impact SETS. In this section, we aim to further
integrate learnings from SETS analysis and EJ practice, to give
interdisciplinary practitioners a map of possible starting points to leverage
them in the context of AI. The discussion that follows engages with the
questions we discussed earlier in Table 2 \- mapping considerations which need
to be integrated in an iterative algorithmic auditing process along the
lifestyle of an AI system. Towards that goal, we identify how scholars within
SETS have grappled with these questions and draw policy recommendations that
we believe could positively contribute to the algorithmic auditing ecosystem.
These are: (1) broadening the inputs and opening-up the outputs of an impact
assessment, (2) enabling meaningful access to redress, and (3) adopting a
place-based and relational approach to the process of evaluating impact.

###  5.1. Broadening the inputs and opening-up the outputs of an impact
assessment

Scholars in SETS have taken a critical approach to integrating lessons between
the fields of socio-ecological systems and socio-technical systems in the
context of sustainability transitions (Leach et al., 2010; Stirling, 2008).
Specifically, by addressing the questions of sustainability governance through
a process of appraisal. The appraisal process is characterized by a particular
methodology, the range or breadth-narrowness of inputs that are included (such
as issues, perspectives, scenarios), and the range of outputs that are
produced and conveyed to a wider audience through a closing-down or opening-up
approach. For example, while a _closed down_ appraisal could result in unitary
prescriptive policy recommendations, a focus on _opening up_ the appraisal
“focuses on neglected issues, includes marginalized perspectives, triangulates
contending knowledges, tests sensitivities to different methods, considers
ignored uncertainties, examines different possibilities, and highlights new
options” (Stirling, 2008).

Through a SETS lens, Leach et al. (2010) call for moving away from narrow and
closed risk-based approaches such as cost-benefit analysis and risk
assessment, towards broad and open framings such as participatory and
deliberative mapping. For example, Burgess et al. (2007) point to the lack of
mechanisms that enable contestation in prior methodologies and develop
deliberative mapping as an approach engaging specialists and citizens in an
appraisal process combining the use of quantitative decision analysis
techniques with participatory deliberation. They use the methodology in
managing the disposal of UK’s radioactive waste which is a fundamental
struggle in the EJ field (Burgess et al., 2004). Following the evolution of
SETS analysis and EJ concerns, we see that participatory methods have been an
active area of debate in issues regarding climate change policy. In recent
work, Itten and Mouter (2022a) provide an overview of different strategies and
develop a framework for participatory value evaluation. Similarly, in
algorithmic auditing, we have seen a growing number of calls for adopting
participatory methods that enable meaningful community participation in the
auditing process as well as disclosure of audit findings (Costanza-Chock et
al., 2022; Sloane et al., 2020; Katell et al., 2020; Shen et al., 2021). We
hope that by drawing parallels in how these questions have been evolved in
other policy domains, AI audit practitioners can leverage key lessons and
opportunities for collaboration.

###  5.2. Enabling meaningful access to redress

Informed by a SETS-oriented approach and recent EJ discourse, the algorithmic
auditing process needs to enable meaningful access to redress of algorithmic
harms. In practice, these could be operationalized through (1) prototyping
mechanisms for reporting of algorithmic incidents, controversies, or injustice
experienced by (un)intended users of AI systems, (2) considering how such
mechanisms are affected by and may contribute to existing power structures,
and finally (3) the role of workers in a growing number of calls for
solidarity.

Within EJ, a growing number of community-led initiatives examine how
environmental data about harms and injustices moves between communities and
policymakers (Nost and Goldstein, 021a; Nost et al., 021b). Recent
developments in EJ have called for critical and decolonial frameworks,
recognizing ”power-dynamics and the complexity of intersectionality of
marginalizations and injustices” (Menton et al., 2020), suggesting that
”rather than simply participation, justice must include self-governing
authority” (Temper, 2019). An algorithmic auditing ecosystem that empowers
(un)intended users to contribute to improved understanding and transparency of
potential algorithmic harms could lead to improved algorithmic accountability.
However, there is a need to enable that through requiring meaningful avenues
for users to report, challenge, contest, and seek redress for experiences of
algorithmic harms. Furthermore, AI auditors conducting an assessment of a
particular algorithmic system would greatly benefit from individual or
collective accounts of experiences of algorithmic bias, harms, or injustice.
Currently such incidents reporting data has predominantly been leveraged in
third-party algorithmic audits lead by independent researchers with no
contractual obligation to the audit target (Costanza-Chock et al., 2022). Shen
et al. (2021) conduct a field scan of examples where users have meaningfully
contributed to third-party audits across particular AI systems including
Google Image Search, Google Maps, YouTube, TikTok, and others. Learning from
the field of EJ, we identify the need to create policy and other mechanisms
that provide avenues for standardizing the participation of users in
algorithmic auditing efforts.

When engaging end users in an algorithmic audit, we need to consider how could
this potentially contribute to existing power asymmetries and center-margin
dynamics between AI companies and impacted intended and unintended users of
their technology. The power-dynamics and justice aspects of participation have
already been at the core of decolonial critiques of the design, development,
and use of algorithmic systems (Agre, 1997; Berg, 1998; Ehsan and Riedl,
2020). Mohamed et al. (2020) explore the role of decolonial theory in critical
analysis of the center-margin dynamics of power structures within an
algorithmic system. Similarly, Ricaurte (2019) incorporates a multidimensional
approach to enable decolonial, intersectional, and feminist analysis of data
colonialism in deconstructing algorithmic violence and empowering resistance.
Furthermore, Sloane et al. (2020) go on to critically examine existing modes
of participation in the design and development of machine learning systems,
cautioning against “participation washing”, that may legitimize exploitative
and extractive forms of community involvement.

Power structures in AI are also being examined from within technology
companies developing large scale AI systems. There is a growing movement and
literature surrounding the role of workers in improving the understanding and
awareness of algorithmic harms (Nedzhvetskaya and Tan, 2022). Within the EJ
field, we have seen initiatives led by employees of technology companies
including Amazon444https://amazonemployees4climatejustice.medium.com/amazon-
employees-are-joining-the-global-climate-walkout-9-20-9bfa4cbb1ce3 and
Microsoft555https://github.com/MSworkers/for.ClimateAction , petitions banning
the use of AI for fossil fuel exploration and
development666https://onezero.medium.com/google-says-it-will-not-build-custom-
a-i-for-oil-and-gas-extraction-72d1f71f42c8, and calls for action from civil
society organizations. Dobbe and Whittaker (2019) illuminate the
interrelationships across many of these efforts in highlighting the role of
tech workers in global EJ movements. Furthermore, they argue for acknowledging
AI companies’ contribution to the climate crisis and discuss recommendations
for policymakers including: (1) mandating transparency, (2) accounting for the
impacts of the supply chain of AI and its computational infrastructure
(Crawford and Joler, 2018; Widder and Nafus, 2022), (3) going beyond
efficiency and being wary of rebound effects (Coulombel et al., 2019), (4)
considering how AI is integrated in non-energy policy domains, (5) integrating
tech regulation and green new deal policy making, (6) curbing the use of AI to
accelerate fossil fuel extraction, and (7) addressing the use of AI to harm
and exclude climate refugees. Inspired by an EJ frame, we hope policy
guardrails in AI could leverage tactics evolved within the EJ movement, both
in terms of enabling tech workers whistleblower protection as well as creating
pathways for contestability and redress of algorithmic harms among communities
disproportionately impacted by AI systems.

###  5.3. Adopting a place-based and relational approach to the process of
evaluating impact

Finally, we discuss place-based and relational approaches to SETS analysis and
highlight their potential impact in the context of algorithmic auditing. A
place-based approach or place-based policy commonly assumes the interactions
between an intervention and a specific physical geography are critical in the
implementation and impact assessment of that intervention (Barca et al.,
2012). SETS scholars have demonstrated the role of place-based research
methods as a key contributor to achieving global sustainability goals (Moran,
2010). For example, Balvanera et al. (2017) argue that “transformations
towards sustainability are often triggered at the local scale through the co-
construction of local solutions.” By analyzing the strengths and challenges to
place-based socio-ecological research, they highlight that a place-based
approach enables the consideration of long-term time horizons, capacity
building, local communities of practice, and a bridge between local and global
sustainability initiatives. Furthermore, SETS scholars are accustomed to
studying systems in relational terms (Nkhata et al., 2008; Lejano, 2019; West
et al., 2020). Recent work has sought to enable practitioners to
operationalize relationality through describing and modeling not just material
but also social processes that drive or impede resilience in a socio-
ecological system, for example in disaster response and recovery (Cagney et
al., 2016) and urban planning (Goldstein et al., 2015).

A growing number of interdisciplinary scholars have articulated a relational
approach in understanding algorithmic impacts (Birhane, 2021; Viljoen, 2021;
Cooper et al., 2022). Furthermore, through a SETS lens, we argue that _place_
is where relational concerns need to be acknowledged, documented, debated, and
ultimately addressed through diverse models of engagement. Being able to
relate to environmental sustainability is often made possible through the
place-based work of grassroots communities and civil society actors who bring
awareness to the socio-economic and socio-ecological hardship and stressors
for people and non-human living beings and ecosystems. Similarly,
investigative journalists and civil society initiatives have often been at the
forefront of algorithmic justice issues experienced by marginalized
communities.

What would it look like for policy makers and AI developers to enable local
communities where the technology is deployed to evolve their own tools and
processes for algorithmic audits? Learning from climate policymaking, we could
leverage existing models for citizen deliberation and online participation
contributing to a more meaningful algorithmic auditing process. For example,
Itten and Mouter (2022b) present a case study from the Netherlands showing the
benefits of combining different participatory approaches in climate action.
Furthermore, AI practitioners and diverse stakeholders could encourage that
algorithmic systems are deployed within new community-lead contractual models
such as the Maori data sovereignty licences (Taiuru, 2021) and the behavioral
use licensing agreements (Contractor et al., 2020). For example, the
Kaitiakitanga777https://papareo.nz/kaitiakitanga affirmative action data
license developed by the Maori community in New Zealand, states that any
benefits derived from their data need to flow back to the source of the data.
Learning from the intentionality within their data stewardship principles and
process, we turn to datasets used broadly by practitioners in AI as benchmarks
for assessing the utility of machine learning models. A justice-oriented
approach could better empower the individuals and communities who have
collected, curated, labeled, or otherwise contributed to creating datasets,
have a voice in how that data is used.

##  6\. Conclusion

Building on a growing number of algorithmic harm investigations and critical
scholarship, we have argued for the need to expand the assumed boundaries of
algorithmic system through understanding them as ontologically indistinct from
social-ecological-technological systems. Thus, our _theory of change_ is
centered on the need and utility of SETS analysis to enable interdisciplinary
collaborations in disentangling the complex nature of algorithmic impacts in
_a more-than human world_. Furthermore, we aim to bring awareness to potential
parallels in the field of algorithmic justice and the longstanding theory and
practice within the environmental justice movement. We overlay a SETS analysis
framework and emerging themes in EJ to motivate a set of questions that
practitioners could operationalize in the algorithmic audit process.
Futhermore, we draw policy recommendations that we believe could positively
contribute to considering environmental sustainability concerns within the
algorithmic auditing ecosystem: (1) broadening the inputs and opening-up the
outputs of an impact assessment, (2) enabling meaningful access to redress,
and (3) adopting a place-based and relational approach to the process of
evaluating impact. Our recommendations are rooted in lessons from the existing
community of practice across algorithmic auditing, SETS analysis, and EJ.
Future work will be required to link the ideas we lay out to existing policy
and practical implementation in particular domains.

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