S Mohamed, MT Png, W Isaac, Decolonial AI: Decolonial Theory
as Sociotechnical Foresight in Artificial Intelligence,
Philosophy and Technology (405), 2020. 405 DOI: 10.1007/s13347-020-00405-8

11institutetext:  Shakir Mohamed 22institutetext: DeepMind, London 22email:
<EMAIL_ADDRESS>33institutetext: Marie-Therese Png 44institutetext:
University of Oxford 44email<EMAIL_ADDRESS>55institutetext:
William Isaac 66institutetext: DeepMind, London 66email:
<EMAIL_ADDRESS>77institutetext: * Equal contributions.
Submitted 16 January, Accepted 26 May 2020
To cite: S. Mohamed, MT. Png and W. Isaac (2020) Decolonial AI: Decolonial
Theory as Sociotechnical Foresight in Artificial Intelligence. Philosophy and
Technology (405).

# Decolonial AI: Decolonial Theory as
Sociotechnical Foresight in Artificial Intelligence

Shakir Mohamed  Marie-Therese Png  William Isaac

###### Abstract

This paper explores the important role of critical science, and in particular
of post-colonial and decolonial theories, in understanding and shaping the
ongoing advances in artificial intelligence. Artificial Intelligence (AI) is
viewed as amongst the technological advances that will reshape modern
societies and their relations. Whilst the design and deployment of systems
that continually adapt holds the promise of far-reaching positive change, they
simultaneously pose significant risks, especially to already vulnerable
peoples. Values and power are central to this discussion. Decolonial theories
use historical hindsight to explain patterns of power that shape our
intellectual, political, economic, and social world. By embedding a decolonial
critical approach within its technical practice, AI communities can develop
foresight and tactics that can better align research and technology
development with established ethical principles, centring vulnerable peoples
who continue to bear the brunt of negative impacts of innovation and
scientific progress. We highlight problematic applications that are instances
of coloniality, and using a decolonial lens, submit three tactics that can
form a decolonial field of artificial intelligence: creating a critical
technical practice of AI, seeking reverse tutelage and reverse pedagogies, and
the renewal of affective and political communities. The years ahead will usher
in a wave of new scientific breakthroughs and technologies driven by AI
research, making it incumbent upon AI communities to strengthen the social
contract through ethical foresight and the multiplicity of intellectual
perspectives available to us; ultimately supporting future technologies that
enable greater well-being, with the goal of beneficence and justice for all.

###### Keywords:

decolonisation coloniality sociotechnical foresight intercultural ethics
critical technical practiceartificial intelligence affective community

††journal: Philosophy and Technology

##  1 How Values Shape Scientific Knowledge and Technology

The ongoing advances in artificial intelligence (AI), and innovations in
technology more generally, encompass ever-larger aspects of the cultural,
economic and political life of modern society. We aim to capture this
expanding role and impact of AI by widening the conceptual aperture with which
AI is understood: dually viewing AI as both object and subject, i.e. viewing
AI as technological artefacts and as systems of networks and institutions,
respectively.

As an object, advances in AI research111This view of AI as object, and the of
the term AI throughout, will be used as an umbrella term that includes the
field of machine learning. Both machine learning and artificial intelligence
are disciplines focused on the science and engineering of intelligent agents
or computer programs (Russell and Norvig, 2016; Boden, 2018). While the
broader field of AI includes both symbolic (also known as classical AI or
GOFAI) and connectionist (e.g., artificial neural networks) research, the
field of machine learning can be defined by research on more tractable machine
tasks leveraging techniques at the intersection of computer science and
statistical inference (Mitchell, 2006). over the last two decades—often
attributed to a combination of increases in computational power, availability
of large amounts of data, and advances in learning algorithms (LeCun et al.,
2015)—has led to novel applications in a wide range of sectors, including
transportation and healthcare, amongst others (Gerrish, 2018). While these
recent innovations have led to some societal benefits, they have also
demonstrated their potential to be abused or misused in ways their designers
could not have imagined (O’Neil, 2016). As a subject, AI has seen itself
elevated from an obscure domain of computer science into technological
artefacts embedded within and scrutinised by governments, industry and civil
society. These stakeholders play a significant role in shaping the future
direction and use of advanced technologies such as AI—whether through the
establishment of regulatory and ethical frameworks or the promotion of
specific algorithmic architectures222Specific applications of machine learning
and AI largely centre on the use of learning algorithms that manipulate and
transform data into information suitable for the given task.—that warrants
consideration under a more expansive conceptualisation of the term AI.

As both object and subject, the aims and applications of AI have been brought
into question. At the heart of these discussions are questions of values and
the power relations in which these values are embedded. What values and norms
should we aim to uphold when performing research or deployment of systems
based on artificial intelligence? In what ways do failures to account for
asymmetrical power dynamics undermine our ability to mitigate identified harms
from AI? How do unacknowledged and unquestioned systems of values and power
inhibit our ability to assess harms and failures in the future?

###  1.1 The Evolution of Value and Power Paradigms

The role that values play in the process of generating new knowledge is a
perennial question, particularly in the sciences. Philosophers have debated
the importance of epistemic values, such as internal consistency,
falsifiability, generalisability of a particular theory, and notions of
scientific objectivity (Laudan, 1968; Bueter, 2015). These values shape the
veracity of scientific statements, aiming to establish broader ontological or
causal claims about the nature of specific systems. Yet, science is a product
not only of epistemic values, but also of contextual values that reflect
moral, societal or personal concerns in the application of scientific
knowledge. There is strong consensus that non-epistemic values have a
legitimate role in scientific reasoning, particularly in the choice of
research projects and the application of scientific results (Elliott and
McKaughan, 2014; Douglas, 2007). This role of contextual values also applies
to the work of computing and technology (Nissenbaum, 2001; Van de Poel and
Kroes, 2014)—a recognition established in the broader field of Values in
Technology (Friedman et al., 2013; Sengers et al., 2005; DiSalvo, 2012).

Due to repeated instances of unethical research practices within the
scientific community—instances like the U.S. Public Health Service Syphilis
Study at Tuskegee (Brandt, 1978)—concerned scientists, policy-makers and human
rights advocates responded by formalising contextual values into ethical
frameworks that reoriented power relations between researchers and impacted
communities or persons. Efforts such as the Nuremberg Code (Nuremberg Military
Tribunals, 1947), the Helsinki declaration (WMA General Assembly, 1964), and
the Belmont Principles (Belmont Commission, 1978) collectively led to the
establishment of three core ethical values or rights that should serve as a
minimum standard for human subject research: respect-for-persons (individual
autonomy), beneficence (research designed to maximise societal benefit and
minimise individual harm), and justice (research risks must be distributed
across society). These principles are viewed as a historical milestone for
research ethics, although their violations continue to occur, e.g., the
ongoing questions of unethical blood exports during the West Africa Ebola
epidemic (Freudenthal, 2019). These principles are also questioned and subject
to many reappraisals, which have highlighted their failures in capturing a
range of emerging or novel harms, or an insufficiency in capturing the lived
realities of under-represented groups (Shore, 2006; Vitak et al., 2016).

The limitations of these value principles become clearer as AI and other
advanced technologies become enmeshed within high-stakes spheres of our
society. Initial attempts to codify ethical guidelines for AI, e.g., the
Asilomar principles (Asilomar Meeting, 2017), focused on risks related to
lethal autonomous weapons systems and AGI Safety. Though both are critical
issues, these guidelines did not recognise that risks in peace and security
are first felt by conflict zones in developing countries (Garcia, 2019), or
engage in a disambiguation of social safety and technical safety. Moreover,
they did not contend with the intersection of values and power, whose values
are being represented, and the structural inequities that result in an unequal
spread of benefits and risk within and across societies.

An example of this nexus between values, power, and AI is a recent study by
Obermeyer et al. (2019), which revealed that a widely used prediction
algorithm for selecting entry into healthcare programs was exhibiting racial
bias against African-American patients. The tool was designed to identify
patients suitable for enrolment into a “high-risk care management” programme
that provides access to enhanced medical resources and support. Unfortunately,
large health systems in the US have emphasised contextual values to “reduce
overall costs for the healthcare system while increasing value” (AMA, 2018) or
“value for money” (UK National Health Service, 2019) when selecting potential
vendors for algorithmic screening tools at the expense of other values such as
addressing inequities in the health system. As a result, the deployed
algorithm relied on the predictive utility of an individual’s health expenses
(defined as total healthcare expenditure) indirectly leading to the rejection
of African-American patients at a higher rate relative to white patients,
denying care to patients in need, and exacerbating structural inequities in
the US healthcare system (Nelson, 2002). As this example shows, the unique
manner in which AI algorithms can quickly ingest, perpetuate, and legitimise
forms of bias and harm represents a step change from previous technologies,
warranting prompt reappraisal of these tools to ensure ethical and socially-
beneficial use.

An additional challenge is that AI can obscure asymmetrical power relations in
ways that make it difficult for advocates and concerned developers to
meaningfully address during development. As Benjamin (2019) notes, “whereas in
a previous era, the intention to deepen racial inequities was more explicit,
today coded inequity is perpetuated precisely because those who design and
adopt such tools are not thinking carefully about systemic racism”. Some
scholars such as Floridi et al. (2018) have highlighted that technologies such
as AI require an expansion of ethical frameworks, such as the Belmont
Principles, to include explicability (explanation and transparency) or non-
malfeasance (do no harm). Whittlestone et al. (2019) conversely argue for a
move away from enumerating new value criteria, and instead highlight the need
to engage more deeply with the tensions that arise between principles and
their implementation in practice. Similarly, we argue that the field of AI
would benefit from dynamic and robust foresight tactics and methodologies
grounded in the critical sciences to better identify limitations of a given
technology and their prospective ethical and social harms.

###  1.2 Critical Science as a Sociotechnical Foresight Tool

The critical science approach represents a loosely associated group of
disciplines that seek to uncover the underlying cultural assumptions that
dominate a field of study and the broader society. Scholarship in this domain
(Winner, 1980; Nissenbaum, 2001; Greene et al., 2019) aims not only to explain
sociotechnical phenomena, but to also examine issues of values, culture and
power at play between stakeholders and technological artefacts. We use a
necessarily broad scope of critical science theories due to the expansive
range of applications of AI, but seek to emphasise particularly the role of
post-colonial and decolonial critical theories. While decolonial studies
begins from a platform of historical colonialism, it is deeply entangled with
the critical theories of race, feminism, law, queerness, and science and
technology studies (D’Ignazio and Klein, 2020; Feenberg, 2017).

The role of values and power as they relate to technology and data has been
argued by a multitude of scholars who draw from the decolonial theories, such
as Ricaurte (2019), Milan and Treré (2019) and Couldry and Mejias (2019a), as
well as established research in post-colonial and decolonial computing, such
as those by Irani et al. (2010), Dourish and Mainwaring (2012) and Ali (2016).
Such critical perspectives are increasingly used to elucidate potential
ethical and social ramifications of AI and technology generally, with much
research now available that exposes concerns of bias and injustice in
algorithmic systems (Angwin et al., 2016; Benjamin, 2019; Buolamwini and
Gebru, 2018; Lum and Isaac, 2016; Noble, 2018; Eubanks, 2018), exploitative or
extractive data practices (Gray and Suri, 2019), and applications of AI that
dispossess the identity and resources of vulnerable populations (Green, 2019;
Keyes, 2018; Hanna et al., 2019; Stark, 2019).

In this paper, our aim is to guide readers through a brief introduction to
decolonial theory, and to demonstrate how this theoretical framework can serve
as a powerful lens of ethical foresight. Technology foresight and foresight
methodologies more broadly is a term used to classify efforts by researchers,
policy-makers and industry practitioners to understand and anticipate how
choices and actions made today can shape or create the future (Coates et al.,
1985). For AI technologies, problematic outcomes in high-stakes domains such
as healthcare or criminal justice have demonstrated a clear need for dynamic
and robust ethical foresight methodologies. Such methodologies could enable
stakeholders to better anticipate and surface blind-spots and limitations,
expand the scope of AI’s benefits and harms, and reveal the relations of power
that underlie their deployment. This is needed in order to better align our
research and technology development with established and emerging ethical
principles and regulation, and to empower vulnerable peoples who, so often,
bear the brunt of negative impacts of innovation and scientific progress.

##  2 Coloniality and Decolonial Theory

Decolonisation refers to the intellectual, political, economic and societal
work concerned with the restoration of land and life following the end of
historical colonial periods (Ashcroft et al., 2006). Territorial
appropriation, exploitation of the natural environment and of human labour,
and direct control of social structures are the characteristics of historical
colonialism. Colonialism’s effects endure in the present, and when these
colonial characteristics are identified with present-day activities, we speak
of the more general concept of coloniality (Quijano, 2000; Mignolo, 2007;
Maldonado-Torres, 2007). This section is a brief review of coloniality, its
view on systems of power, and its manifestation in the digital world.

Coloniality is what survives colonialism (Ndlovu-Gatsheni, 2015). Coloniality
therefore seeks to explain the continuation of power dynamics between those
advantaged and disadvantaged by “the historical processes of dispossession,
enslavement, appropriation and extraction […] central to the emergence of the
modern world” (Bhambra et al., 2018). Coloniality names the continuity of
established patterns of power between coloniser and colonised—and the
contemporary remnants of these relationships—and how that power shapes our
understanding of culture, labour, intersubjectivity, and knowledge production;
what Quijano (2000) refers to as the coloniality of power. For Quijano (2000)
the power of coloniality lies in its control over social structures in the
four dimensions of authority, economy, gender and sexuality, and knowledge and
subjectivity. Similarly, for Maldonado-Torres (2007), coloniality is the
reproduction of hierarchies of race, gender and geopolitics, which were
invented or instrumentalised as tools of colonial control. For Couldry and
Mejias (2019a), who bring the coloniality of power into the digital present,
it is modern data relations—the human relations that when captured as data
enables them to become a commodity—that “recreate a colonising form of power”.

Consequently, decolonisation takes two roles. The first is a territorial
decolonisation that is achieved by the dissolution of colonial relations. The
second, is a structural decolonisation, with which this paper is concerned,
that seeks to undo colonial mechanisms of power, economics, language, culture,
and thinking that shapes contemporary life: interrogating the provenance and
legitimacy of dominant forms of knowledge, values, norms and assumptions.
Three views clarify this decolonial knowledge landscape.

  * •

A decentring view of decolonisation seeks to reject an imitation of the West
in all aspects of life, calling for the assertion of unique identities and a
re-centring of knowledge on approaches that restore global histories and
problems and solutions. For Ngũgĩ wa Thiong’o, this means replacing the
English language as the unassailable medium of teaching and discourse (Wa
Thiong’o, 1992). Discussions on decolonising the curriculum or decolonising
the university call for reappraisals of what is considered the foundation of
an intellectual discipline by emphasising and recognising the legitimacy of
marginalised knowledge (Jansen, 2019; Bhambra et al., 2018); calls to
decolonise science often invoke this view of decolonisation (Harding, 2011).

  * •

An additive-inclusive view continues to use existing knowledge, but in ways
that recognises explicitly the value of new and alternative approaches, and
that supports environments in which new ways of creating knowledge can
genuinely flourish. This view is invoked by works that criticise universalism
in thinking, and instead advocate for localisation and pluriversalism
(Mignolo, 2012; Escobar, 2011).

  * •

An engagement view calls directly for more critical views of science. This
view calls on us to examine scientific practice from the margins, to place the
needs of marginalised populations at the centre of the design and research
process, and to ask where knowledge comes from—who is included and left out,
in whose interest is science applied, who is silenced, and what unacknowledged
assumptions might be at play (McDowell and Chinchilla, 2016).

Decolonial theory provides us with several useful tools with which to qualify
the nature of power imbalances or inequitable impacts that arise from advanced
technologies like AI. One such framework identifies metropoles—the centres of
power—and their peripheries that hold relatively less power and contest the
metropole’s authority, participation and legitimacy in shaping everyday life
(Champion, 2005). Dependency theory expands on this framework, by tying
colonial histories to present day underdevelopment and continued economic
imbalance between countries and regions, as well as tying resulting
dependencies to historic metropole and periphery dynamics (Champion, 2005).
Using the lens of metropole and periphery, we can identify contemporary
practices in AI development partially as features of colonial continuities
from states and governments. Similarly, today’s technology corporations could
be described as metropoles of technological power with civic society and
consumers sitting at the periphery.

Metropole-periphery dichotomies are interpretive models that if not used
carefully can reduce the reality of lived experiences to overly-simplified
binaries of ‘West and the rest’, ‘North and South’, ‘powerful and oppressed’
(McClintock, 1992; Stoler, 2008; Thrush, 2008), exposing some of the
limitations of decolonial theory. In addition, grand historical meta-
narratives of injustice, contending with the theoretical idea of ‘global’, and
‘speaking for the oppressed’ (Pappas, 2017) are pitfalls that must be avoided.
A needed balance can be found by incorporating other modes of decolonial
thought, such as contrapuntal analysis (Said, 1993), psychodynamic
perspectives (Fanon, 1961; Nandy, 1989), economic analysis (Pollard et al.,
2011), and historical and literary criticism (James, 1993; Gopal, 2019),
amongst others. Because of the limitations of the theory, we believe it is
important to incorporate the wider critical science view introduced in the
previous section.

##  3 Algorithmic Coloniality

By recognising the analogues of territorial and structural coloniality in the
digital age, we propose the application of decolonial theory to digital
technologies such as AI. Digital spaces—created by the internet and the
increasingly-networked systems and devices we use—form digital territories
that, like physical spaces, have the propensity to become sites of extraction
and exploitation, and thus the sites of digital-territorial coloniality. The
coloniality of power can be observed in digital structures in the form of
socio-cultural imaginations, knowledge systems, and ways of developing and
using technology which are based on systems, institutions, and values that
persist from the past and remain unquestioned in the present. As such,
emerging technologies like AI are directly subject to coloniality, giving
decolonial critical theories a powerful analytical role.

The emerging theories of data colonialism (Thatcher et al., 2016; Ricaurte,
2019; Couldry and Mejias, 2019a) and data capitalism (Zuboff, 2019) recognise
this nature of historic continuity and the role of data as the material
resource that is exploited for economic expansion. Ricaurte (2019) develops a
theoretical model that analyses the coloniality of technological power through
data, examining data-centric epistemologies as an expression of the
coloniality of power, in how they impose “ways of being, thinking, and feeling
that leads to the expulsion of human beings from the social order, denies the
existence of alternative worlds and epistemologies, and threatens life on
Earth” (Ricaurte, 2019). Couldry and Mejias (2019b) further expand on the
colonial continuities of extraction and exploitation of land, labour and
relations through digital infrastructure. This larger area of technological
coloniality is further developed by scholars in areas of intersectional data
feminism (D’Ignazio and Klein, 2020), critical race theory (Benjamin, 2019),
decolonisation of technology (Awori et al., 2016), new data epistemologies
(Milan and Van der Velden, 2016), and environmental sustainability and justice
(Røpke, 2001; Ricaurte, 2019; Gallopin, 1992).

We use the term algorithmic coloniality to build upon data colonialism in the
context of the interactions of algorithms across societies, which impact the
allocation of resources, human socio-cultural and political behaviour, and
extant discriminatory systems. We also begin to examine how coloniality
features in algorithmic decision-making systems as they generate new labour
markets, impact geopolitical power dynamics, and influence ethics discourse.

In the following section, we introduce the language of decoloniality to the
current discourse on fairness, accountability, and transparency in algorithmic
systems, as well as introduce a taxonomy of decolonial foresight:
institutionalised algorithmic oppression, algorithmic exploitation, and
algorithmic dispossession. Within these forms of decolonial foresight, we
present a range of use-cases that we identify as sites of coloniality:
algorithmic decision systems, ghost work, beta-testing, national policies, and
international social development. By sites of coloniality we mean cases that
exhibit structural inequalities that can be contextualised historically as
colonial continuities. These sites of coloniality help identify where
empirical observation departs from the current theoretical frameworks of power
in AI, which by-and-large are ahistorical. By using these sites to address the
clash of theory and empiricism, we argue that discussions of power and
inequality as related to AI cannot be ahistorical and are incomplete if they
fail to recognise colonial continuities.

###  3.1 Algorithmic Oppression

Algorithmic oppression extends the unjust subordination of one social group
and the privileging of another—maintained by a “complex network of social
restrictions” ranging from social norms, laws, institutional rules, implicit
biases, and stereotypes (Taylor, 2016)—through automated, data-driven and
predictive systems. The notion of algorithmic or automated forms of oppression
has been studied by scholars such as Noble (2018) and Eubanks (2018). The
following examples will make initial connections between instances of
algorithmic oppression across geographies, and identify the role of decolonial
theory in this discourse.

#### Site 1: Algorithmic Decision Systems

Predictive systems leveraging AI have led to the formation of new types of
policing and surveillance, access to government services, and reshaped
conceptions of identity and speech in the digital age. Such systems were
developed with the ostensible aim of providing decision-support tools that are
evidence-driven, unbiased and consistent. Yet, evidence of how these tools are
deployed shows a reality that is often the opposite. Instead, these systems
risk entrenching historical injustice and amplify social biases in the data
used to develop them (Benjamin, 2019).

Evidence of such instances are abundant. As an example, digital human rights
concerns have been widely-raised: in Singapore’s application of facial
recognition in CCTV through the Lamppost as a Platform initiative (LaaP)
(Johnston, 2019), New Delhi’s CMAPS predictive policing system (Marda and
Narayan, 2020), India’s Aadhaar identity system (Siddiqui and Singh, 2015),
the Kenyan Huduma Namba digital/biometric identity system (Nyawa, 2019), and
the welfare interventions for Māori children by the New Zealand government
(Vaithianathan et al., 2013; Gavighan et al., 2019). The impact of predictive
algorithms on people, from everyday citizens to the most vulnerable,
highlights the need for diversified and contextualised approaches to issues of
justice and fairness in automated systems.

Algorithmic decision systems (Isaac, 2017) are increasingly common within the
US criminal justice system despite significant evidence of shortcomings, such
as the linking of criminal datasets to patterns of discriminatory policing
(Angwin et al., 2016; Lum and Isaac, 2016; Richardson et al., 2019). Beyond
the domain of criminal justice, there are numerous instances of predictive
algorithms perpetuating social harms in everyday interactions, including
examples of facial recognition systems failing to detect Black faces and
perpetuating gender stereotypes (Buolamwini and Gebru, 2018; Keyes, 2018;
Stark, 2019), hate speech detection algorithms identifying Black and queer
vernacular as ‘toxic’ (Sap et al., 2019), new recruitment tools discriminating
against women (Dastin, 2018), automated airport screening-systems
systematically flagging trans bodies for security checks (Costanza-Chock,
2018), and predictive algorithms used to purport that queerness can be
identified from facial images alone (Agüera y Arcas et al., 2018).

The current discourse on fairness, accountability and transparency in
sociotechnical systems333For example, see ACM Conference on Fairness,
Accountability, and Transparency., under which many of these cases are
discussed, can be further enriched if these inequities are historically
contextualised in global systems of racial capitalism, class inequality, and
heteronormative patriarchy, rooted in colonial history (Bhattacharyya et al.,
2018; Sokoloff and Pincus, 2008). In the case of racial capitalism, similarly
to Ricaurte (2019), Couldry and Mejias (2019a), and Milan and Treré (2019),we
propose that institutionalised harms replicated by automated decision-making
tools should be understood as continuous to, and inextricably linked with,
“histories of racist expropriation”, and that “only by tracking the
interconnections between changing modes of capitalism and racism that we can
hope to address the most urgent challenges of social injustice” (Bhattacharyya
et al., 2018).

A decolonial framework helps connect instances of algorithmic oppression to
wider socio-political and cultural contexts, enabling a geographically,
historically and intersectionally expansive analysis of risks and
opportunities pertaining to AI systems. Notably, it allows for the analysis to
move beyond North American or European identity frameworks or definitions of
harms. By connecting instances of algorithmic oppression across geographies,
new approaches that consider alternative possibilities of using technology in
socially complex settings in more critical and considered ways will emerge,
and so too will designs that incorporate inclusive and well-adapted mechanisms
of oversight and redress from the start.

###  3.2 Algorithmic Exploitation

Algorithmic exploitation considers the ways in which institutional actors and
industries that surround algorithmic tools take advantage of (often already
marginalised) people by unfair or unethical means, for the asymmetrical
benefit of these industries. The following examples examine colonial
continuities in labour practices and scientific experimentation in the context
of algorithmic industries.

#### Site 2: Ghost Workers

Many of the recent successes in AI are possible only when the large volumes of
data needed are annotated by human experts to expose the common-sense elements
that make the data useful for a chosen task. The people who do this labelling
for a living, the so called ‘ghost workers’ (Gray and Suri, 2019), do this
work in remote settings, distributed across the world using online annotation
platforms or within dedicated annotation companies. In extreme cases, the
labelling is done by prisoners (Hao, 2019) and the economically vulnerable
(Yuan, 2018), in geographies with limited labour laws. This is a complicated
scenario. On one hand such distributed work enables economic development,
flexibility in working, and new forms of rehabilitation. On the other, it
establishes a form of knowledge and labour extraction, paid at very low rates,
and with little consideration for working conditions, support systems and
safeties.

A decolonial lens shifts our view towards understanding how colonial history
affects present day labour regulation and enforcement (Ronconi, 2015), and how
the capacity to mobilise production and outsource services across borders
allows industries to take advantage of present-day post-colonial economic
inequalities in order to “reorganize production in ways and places that reduce
manufacturing costs and enhance corporate profit” (Gomberg-Muñoz, 2018;
Wallerstein, 1987). Logics of colonial extraction and exploitation have
“mutated but also maintain continuity in the present day”, supporting post-
colonial economic inequalities which have been empirically demonstrated to be
tied to historic colonial activity (Bruhn and Gallego, 2012; Fanon, 1961).
Data generation and processing presents opportunities for extraction and
excavation within data mining industries, which are arguably “ingrained in
practices and techniques of extraction [and] is a kind of colonial imprint”
(Mezzadra and Neilson, 2017), as demonstrated in part by the location of many
ghost workers in previously colonised geographies.

#### Site 3: Beta-testing

There is a long and well-documented history on the exploitation of
marginalised populations for the purpose of scientific and technological
progress. Colonies of the British empire “provided a laboratory for
experimenting with new forms of medical and scientific practice” (Senior,
2018; Tilley, 2014). There has been a historic continuity of scientific
experimentation on African Americans, from early experimentation on black
enslaved women and infants in the 19th century that is foundational to the
field of gynaecology (Washington, 2006), to the Tuskegee syphilis study
(Brandt, 1978). Such experimental practices continue to colour the
establishment of socio-economic development schemes in previously colonised
countries, often by former colonisers (Bonneuil, 2000).

It is with this historic lens that we examine the practice of beta-testing,
which is the testing and fine-tuning of early versions of software systems to
help identify issues in their usage in settings with real users and use-cases.
In the testing of predictive systems we find several clearly exploitative
situations, where organisations use countries outside of their own as testing
grounds—specifically because they lack pre-existing safeguards and regulations
around data and its use, or because the mode of testing would violate laws in
their home countries (UNCTAD, 2013). This phenomenon is known as ethics
dumping: the export of harms and unethical research practices by companies to
marginalised and vulnerable populations or to low and middle income countries,
and which often aligns “with the old fault lines of colonialism” (Schroeder et
al., 2018). The counterpoint to ethics dumping is ethics shirking: what is not
done to protect people when harms emerge beyond what is demanded from legal or
regulatory frameworks (Floridi, 2019).

As an example, Cambridge Analytica (CA) elected to beta-test and develop
algorithmic tools for the 2017 Kenyan and 2015 Nigerian elections, with the
intention to later deploy these tools in US and UK elections. Kenya and
Nigeria were chosen in part due to the weaker data protection laws compared to
CA’s base of operations in the United Kingdom—a clear example of ethics
dumping. These systems were later found to have actively interfered in
electoral processes and worked against social cohesion (Nyabola, 2018). A
critical decolonial approach would, for example, lead us to ask early on why
the transgression of democratic processes by companies such as CA only gained
international attention and mobilisation after beginning to affect western
democratic nations.

In another case of beta-testing, the deployment of predictive algorithms for
child welfare interventions by the New Zealand government initially targeted
Māori, the indigenous people of New Zealand, who have long experienced
institutional racism (Vaithianathan et al., 2013; Gavighan et al., 2019).
Analogously, the data analytics firm Palantir was found to have experimentally
deployed predictive algorithms in the city of New Orleans (in concert with the
police department) without public approval. These tools were used to target
specific individuals and neighbourhoods for police surveillance, and
disproportionately impacted African-Americans (Bullington and Lane, 2018).
These are all cases that cannot be viewed ahistorically, e.g., for African
Americans there is a historic continuity from 19th century gynaecology
experimentation, to the 20th century Tuskegee experiments, to 21st century
predictive policing and beta-testing.

The perspective of historic continuity provided by decolonial theory raises
important questions of accountability, responsibility, contestation, and
recourse, which become increasingly necessary in entangled settings of low
regulation, combined with deficits of localised expertise and contextualised
historic knowledge within firms expanding into new markets. Risks are likely
to arise if we neglect to explore the current variation of ethical standards
based on identity and geography, as well as how algorithms and automated
systems interact with existing social stratification at both local and global
levels.

###  3.3 Algorithmic Dispossession

Algorithmic dispossession, drawing from Harvey (2004) and Thatcher et al.
(2016), describes how, in the growing digital economy, certain regulatory
policies result in a centralisation of power, assets, or rights in the hands
of a minority and the deprivation of power, assets, or rights from a
disempowered majority. The following examples examine this process in the
context of international AI governance (policy and ethics) standards, and AI
for international social development.

#### Site 4: National Policies and AI Governance

Power imbalances within the global AI governance discourse encompasses issues
of data inequality and data infrastructure sovereignty, but also extends
beyond this. We must contend with questions of who any AI regulatory norms and
standards are protecting, who is empowered to project these norms, and the
risks posed by a minority continuing to benefit from the centralisation of
power and capital through mechanisms of dispossession (Thatcher et al., 2016;
Harvey, 2004). As Jasanoff and Hurlbut (2018) remind us, we must be mindful of
“who sits at the table, what questions and concerns are sidelined and what
power asymmetries are shaping the terms of debate”.

A review of the global landscape of AI ethics guidelines (Jobin et al., 2019)
pointed out the “under-representation of geographic areas such as Africa,
South and Central America and Central Asia” in the AI ethics debate. The
review observes a power imbalance wherein “more economically developed
countries are shaping this debate more than others, which raises concerns
about neglecting local knowledge, cultural pluralism and the demands of global
fairness”. A similar dynamic is found when we examine the proliferation of
national policies on AI in countries across the world (Dutton, 2018). In some
views, this is a manifestation of a new type of geopolitics amongst ‘AI
superpowers’ (Lee, 2018), and a rise of ‘AI nationalism’, where nations
wrangle to spread a preferred view of policy, applied approaches and technical
services (Hogarth, 2018; Edgerton, 2007b). We are quickly led to one possible
scene of coloniality by Lee (2017): “Unless they [developing countries] wish
to plunge their people into poverty, they will be forced to negotiate with
whichever country supplies most of their AI software—China or the United
States—to essentially become that country’s economic dependent”. It can be
argued that the agency of developing countries is in these ways undermined,
where they “cannot act unilaterally to forge their own rules”and cannot expect
prompt protection of their interests (Pathways for Prosperity, 2019).

Such concerns were demonstrated at the 2019 G20 summit, where a number of
developing countries including India, Indonesia and South Africa refused to
sign the Osaka Track, an international declaration on data flows (Kanth,
2019), because the interests, concerns and priorities of these countries were
not seen to be represented in the document. The undermining of interests and
agency of developing countries is also a relevant issue vis à vis the OECD AI
Principles (OECD, 2019). As these guidelines are adopted and enforced by
partner countries around the world, we see analogous concerns surfacing around
exclusionary path-dependencies and first-mover advantages (Pathways for
Prosperity, 2019). Additionally, AI governance guidelines risk being
replicated across jurisdictions in a way that may be incompatible with the
needs, goals and constraints of developing countries, despite best efforts
(Pathways for Prosperity, 2019).

There are clear hierarchies of power within these cases of policy development,
which can be analysed using the aforementioned metropole-periphery model. It
is metropoles (be it government or industry) who are empowered to impose
normative values and standards, and may do so at the “risk of forestalling
alternative visions” (Greene et al., 2019). A metropole-periphery model draws
attention to the need to represent values, interests, concerns and priorities
of resource-constrained countries in AI governance processes, as well as the
historic dynamics that prevent this. Decolonial theory offers AI policy makers
a framework to interrogate imbalances of power in AI policy discourse,
understand structural dependencies of developing countries, question ownership
of critical data infrastructures, and assess power imbalances in product
design/development/deployment of computational technologies (Irani et al.,
2010) as well as the unequal distribution of risks and economic benefits.

#### Site 5: International Social Development

Much of the current policy discourse surrounding AI in developing countries is
in economic and social development where advanced technologies are propounded
as solutions for complex developmental scenarios, represented by the growing
areas of AI for Good and AI for the Sustainable Development Goals (AI4SDGs)
(Vinuesa et al., 2020; Floridi et al., 2018; Tomašev et al., 2020). In this
discourse, Green (2019) proposes that “good isn’t good enough”, and that there
is a need to expand the currently limited and vague definitions within the
computer sciences of what ‘social good’ means.

To do so, we can draw from existing analysis of ICT for Development, which are
often based on historical analysis and decolonial critique (Irani et al.,
2010; Toyama, 2015). These critiques highlight concerns of dependency,
dispossession, or ethics dumping and shirking, as discussed earlier (Schroeder
et al., 2018). Such critiques take renewed form as AI is put forward as a
needed tool for social development. Where a root cause of failure of
developmental projects lies in default attitudes of paternalism, technological
solutionism and predatory inclusion, e.g., ‘surveillance humanitarianism’
(Latonero, 2019; Vinuesa et al., 2020), decolonial thinking shifts our view
towards systems that instead promote active and engaged political community.
This implies a shift towards the design and deployment of AI systems that is
driven by the the agency, self-confidence and self-ownership of the
communities they work for, e.g, adopting co-development strategies for
algorithmic interventions alongside the communities they are deployed in
(Katell et al., 2020).

Co-development is one potential strategy within a varied toolkit supporting
the socio-political, economic, linguistic and cultural relevance of AI systems
to different communities, as well as shifting power asymmetries. A decolonial
view offers us tools with which to engage a reflexive evaluation and
continuous examination of issues of cultural encounter, and a drive to
question the philosophical basis of development (Kiros, 1992). With a self-
reflexive practice, initiatives that seek to use AI technologies for social
impact can develop the appropriate safeguards and regulations that avoid
further entrenching exploitation and harm, and can conceptualise long-term
impacts of algorithmic interventions with historical continuities in mind.

##  4 Tactics for a Decolonial AI

By fusing the fields of artificial intelligence and decolonial theories we can
take advantage of historical hindsight to develop new tools of foresight and
practice. In so doing, we can establish a decolonial AI that can re-create the
field of artificial intelligence in ways that strengthens its empirical basis,
while anticipating and averting algorithmic colonialism and harm.

The five sites of coloniality in the previous section cast the applications of
AI research (its products and predictions—AI as object) and the structures
that support it (data, networks and policies—AI as subject) as expressions of
the coloniality of power (Quijano, 2000, 2007; Mignolo, 2007; Maldonado-
Torres, 2007; Ndlovu-Gatsheni, 2015), and of technological power (Ricaurte,
2019; Couldry and Mejias, 2019a; Ali, 2016). This leads us to seek the
decolonisation of power, whose aim is dismantle harmful power asymmetries and
concepts of knowledge, turning us instead towards a “pluriversal epistemology
of the future” (Mignolo, 2012) that unlike universalisms, acknowledges and
supports a wider radius of socio-political, ecological, cultural, and economic
needs.

In this final section, we aim to develop sets of tactics for the future
development of AI, which we believe open many areas for further research and
action. Tactics do not lead to a conclusive solution or method, but instead to
the “contingent and collaborative construction of other narratives” (Philip et
al., 2012). Our tactics resonate with the proposals for reforming epistemic
practice articulated by many other scholars. Couldry and Mejias (2019a) put
forward a vision for decolonising data relations by exploring six
tasks—reframing what data is for, restoring well-being, naming alternative
world views, gendering, protecting, and creating new forms of social
relations—that must all be oriented towards social goals. Ricaurte (2019)
points to needed change in data governance and regimes, addressing
technological sovereignty and agency, addressing the impact of technological
systems on ecological systems, and the need to imagine alternative digital
futures. Benjamin (2019) asks us to retool solidarity and reimagine justice,
by rethinking design, developing coded equity audits, and developing
abolitionist tools that reimagine technology.

We submit three tactics for future AI design—supporting a critical technical
practice of AI, establishing reciprocal engagements and reverse pedagogies,
and the renewal of affective and political community—based on lessons of
resistance and recovery from historical and decolonial criticism, and grounded
within already-existing work that shows how these tactics might be enacted in
practice.

###  4.1 Towards a Critical Technical Practice of AI

The basis of decolonial AI rests in a self-reflexive approach to developing
and deploying AI that recognises power imbalances and its implicit value-
systems. It is exactly this type of framework that was developed by Agre
(1997), who described a shift towards a Critical Technical Practice of AI
(CTP). Critical technical practices take a middle ground between the technical
work of developing new AI algorithms and the reflexive work of criticism that
uncovers hidden assumptions and alternative ways of working. CTP has been
widely influential, having found an important place in human-computer
interactions (HCI) and design (Dourish et al., 2004; Sengers et al., 2006). By
infusing CTP with decoloniality we can place a productive pressure on our
technical work, moving beyond good-conscience design and impact assessments
that are undertaken as secondary tasks, to a way of working that continuously
generates provocative questions and assessments of the politically-situated
nature of AI.

The role of practice in this view is broad by necessity. Recent research, in
both AI and Science and Technology Studies (STS), highlights the limitations
of purely technological approaches to addressing the ethical and social
externalities of AI. Yet, technical approaches can meaningfully contribute
when they appropriately reflect the values and needs of relevant stakeholders
and impacted groups (Selbst et al., 2019). This context-aware technical
development that CTP speaks to—which seeks to consider the interplay between
social, cultural, and technical elements—is often referred to as heterogeneous
engineering (Law et al., 1987). As a result, a heterogeneous-critical practice
must encompass multiple approaches for action: in research, organising,
testing, policy, and activism. We explore five topics constituting such a
practice: algorithmic fairness, AI safety, equity and diversity, policy-
making, and AI as a decolonising tool.

Fairness. Research in algorithmic fairness (Nissenbaum, 2001; Dwork et al.,
2012; Barocas and Selbst, 2016) has recognised that efforts to generate a fair
classifier can still lead to discriminatory or unethical outcomes for
marginalised groups, depending on the underlying dynamics of power; because a
‘true’ definition of fairness is often a function of political and social
factors. Quijano (2000) again speaks to us, posing questions of who is
protected by mainstream notions of fairness, and to understand the exclusion
of certain groups as “continuities and legacies of colonialism embedded in
modern structures of power, control, and hegemony”. Such questions speak to a
critical practice whose recent efforts, in response, have proposed fairness
metrics that attempt to use causality (Chiappa and Isaac, 2018; Mitchell et
al., 2018; Nabi and Shpitser, 2018; Madras et al., 2019) or interactivity
(Canetti et al., 2019; Jung et al., 2019) to integrate more contextual
awareness of human conceptions of fairness.

Safety. The area of technical AI safety (Amodei et al., 2016; Raji and Dobbe,
2020) is concerned with the design of AI systems that are safe and
appropriately align with human values. The philosophical question of value
alignment arises, identifying the ways in which the implicit values learnt by
AI systems can instead be aligned with those of their human users. A
specification problem emerges when there is a mismatch between the ideal
specification (what we want an AI system to do) and the revealed specification
(what the AI system actually does). This again raises questions that were
posed in the opening of whose values and goals are represented, and who is
empowered to articulate and embed these values—introducing discussions of
utilitarian, Kantian, and volitional views on behaviour, and on the prevention
and avoidance of undesirable and unintended consequences (Gabriel, 2020). Of
importance here, is the need to integrate discussions of social safety
alongside questions of technical safety.

Diversity. With a critical lens, efforts towards greater equity, diversity and
inclusion (EDI) in the fields of science and technology are transformed from
the prevailing discourse that focuses on the business case of building more
effective teams or as being a moral imperative (Rock and Grant, 2016), into
diversity as a critical practice through which issues of homogenisation,
power, values, and cultural colonialism are directly confronted. Such
diversity changes the way teams and organisations think at a fundamental
level, allowing for more intersectional approaches to problem-solving to bee
taken (D’Ignazio and Klein, 2020).

Policy. There is growing traction in AI governance in developing countries to
encourage localised AI development, such as the initiatives by UNESCO, UN
Global Pulse’s AI policy support in Uganda and Ghana (ITU, 2019) and Sierra
Leone’s National Innovation and Digital Strategy (DSTI, 2019), or in
structuring protective mechanisms against exploitative or extractive data
practices (Gray and Suri, 2019). Although there are clear benefits to such
initiatives, international organisations supporting these efforts are still
positioned within metropoles, maintaining the need for self-reflexive
practices and considerations of wider political economy (Pathways for
Prosperity, 2019).

Resistance. The technologies of resistance have often emerged as a consequence
of opposition to coloniality, built by self-organising communities to “bypass
dynamics and control of the state and corporations” (Steiner, 1994; Milan,
2013). A renewed critical practice can also ask the question of whether AI can
itself be used as a decolonising tool, e.g., by exposing systematic biases and
sites of redress. For example, Chen et al. (2019) instantiate this idea of
using AI to assess systemic biases in order to reduce disparities in medical
care, by studying mortality and 30-day psychiatric readmission with respect to
race, gender, and insurance payer type as a proxy for socioeconomic status.
Furthermore, although AI systems are confined to a specific sociotechnical
framing, we believe that they can be used as a decolonising tool while
avoiding a techno-solutionism trap. When AI systems can be adapted to locally-
specific situations in original ways, they can take a renewed role as ‘creole
technologies’ that find positive and distinctive use at scale, and outside
their initially-conceived usage (Edgerton, 2007a).

###  4.2 Reciprocal Engagements and Reverse Tutelage

Research in post-colonial studies increasingly highlights the essential role
that colonised peoples themselves, through insurgence, activism and
organisation, had in changing the colonial view in the metropole (Gopal, 2019;
Gandhi, 2006). Despite colonial power, the historical record shows that
colonialism was never only an act of imposition. In a reversal of roles, the
metropole often took lessons from the periphery, establishing a reverse
tutelage between centre and periphery. A modern critical practice would seek
to use this decolonial imperative to develop a double vision: actively
identifying centres and peripheries that make reverse tutelage and the
resulting pedagogies of reciprocal exchange part of its foundations, while
also seeking to undo colonial binarisms.

Reverse tutelage directly speaks to the philosophical questions of what
constitutes knowledge. There remains a tension between a view of knowledge as
absolute and of data that, once enough is collected, allows us to form
complete and encompassing abstractions of the world, versus a view of
knowledge that is always incomplete and subject to selections and
interpretation under differing value systems. These oppositional views of
knowledge have been explored in different ways, such as in the important
anthropological work by Forsythe (1993, 2001) on knowledge in AI, in the
genealogy of statistics as ‘the moral science’ (Hacking, 2015), and through
‘new data epistemologies’ (Milan and Van der Velden, 2016). Deciding what
counts as valid knowledge, what is included within a dataset, and what is
ignored and unquestioned, is a form of power held by AI researchers that
cannot be left unacknowledged. It is in confronting this condition that
decolonial science, and particularly the tactic of reverse tutelage, makes its
mark. We put forward three modes—of dialogue, documentation, and
design—through which reciprocal tutelage can be enacted.

Dialogue. Reverse pedagogies create a decolonial shift from paternalistic
towards solidaristic modes of working that can be achieved by systems of
meaningful intercultural dialogue. Such dialogue is core to the field of
intercultural digital ethics, which asks questions of how technology can
support society and culture, rather than becoming an instrument of cultural
oppression and colonialism (Capurro, 2018). Intercultural ethics emphasises
the limitations and coloniality of universal ethics—often the generalisation
of dominant rather than inclusive ethical frameworks—and finds an alternative
in pluralism, pluriversal ethics and local designs (Escobar, 2011; Ess, 2006).
One approach to reverse pedagogies is invoked by Arora (2019) in the field of
privacy research, by interrogating the empirical basis of privacy studies, and
calling for an ‘epistemic disobedience’ and a reliance on shifting roles of
the metropole and periphery.

Documentation. New frameworks have been developed that make explicit the
representations of knowledge assumed within a data set and within deployed AI-
systems. Data sheets for data sets aim to summarise what is and is not
contained within a data set (Gebru et al., 2018), and similar explicit
assessments for AI systems exist using the model cards framework (Mitchell et
al., 2019). The example in Mitchell et al. (2019) on toxicity scoring provides
a simple and powerful example of reverse pedagogy, wherein affected users
exposed the system’s limitations that led to documented improvements in its
subsequent releases.

Design. There is now also a growing understanding of approaches for meaningful
community-engaged research (Mikesell et al., 2013), using frameworks like the
IEEE Ethically Aligned Design (IEEE Global Initiative, 2016), technology
policy design frameworks like Diverse Voices (Young et al., 2019), and
mechanisms for the co-development of algorithmic accountability through
participatory action research (Katell et al., 2020). The framework of
citizens’ juries have also been used to gain insight into the general public’s
understanding of the role and impact of AI (Balaram et al., 2018).

A critical viewpoint may not have been the driver of these solutions, and
these proposals are themselves subject to limitations and critique, but
through an ongoing process of criticism and research, they can lead to
powerful mechanisms for reverse tutelage in AI design and deployment.

###  4.3 Renewed Affective and Political Communities

How we build a critical practice of AI depends on the strength of political
communities to shape the ways they will use AI, their inclusion and ownership
of advanced technologies, and the mechanisms in place to contest, redress and
reverse technological interventions. The systems we described in section 3,
although ostensibly developed to support human decision-makers and
communities, failed to meaningfully engage with the people who would be the
targets of those systems, cutting off these avenues of ownership, inclusion
and justice. The historical record again shows that these situations manifest
through paternalistic thinking and imbalances in authority and choice;
produced by the hierarchical orders of division and binarisation established
by coloniality (Gopal, 2019; Said, 1993; Fanon, 1967; Nandy, 1989). The
decolonial imperative asks for a move from attitudes of technological
benevolence and paternalism towards solidarity. This principle enters amongst
the core of decolonial tactics and foresight, speaking to the larger goal of
decolonising power.

The challenge to solidarity lies in how new types of political community can
be created that are able to reform systems of hierarchy, knowledge, technology
and culture at play in modern life. One tactic lies in embedding the tools of
decolonial thought within AI design and research. Contrapuntal analysis (Said,
1993) is one important critical tool that actively leads us to expose the
habits and codifications that embed questionable binarisms—of metropole and
periphery, of west and the rest, of scientists and humanist, of natural and
artificial—in our research and products. Another tactic available to us lies
in our support of grassroots organisations and in their ability to create new
forms of affective community, elevate intercultural dialogue, and demonstrate
the forms of solidarity and alternative community that are already possible.
Many such groups already exist, particularly in the field of AI, such as Data
for Black Lives (Goyanes, 2018), the Deep Learning Indaba (Gershgorn, 2019),
Black in AI and Queer in AI, and are active across the world.

The advantage of historical hindsight means that we can now recover the
principles of living that were previously made incompatible with life by
colonial binaries. Friendship quickly emerges as a “lost trope of anticolonial
thought” (Gandhi, 2006). This is a political friendship that has been expanded
in many forms: by El Khayat and Khatibi (2010) using the concept of aimance ,
in the politics of friendship (Derrida, 1993), and as affective communities
(Gandhi, 2006) in which developers and users seek alliances and connection
outside possessive forms of belonging. Other resources are also available:
within the principles of political love (Fanon, 1967; Zembylas, 2017; Butorac,
2018), in moral philosophies that recognise both material and immaterial
development (Kiros, 1992), and in philosophies such as Ubuntu (Ramose, 1999)
and Oneness (Wong, 2012). And the decolonial principles we described—moves
from benevolence to solidarity, enabling systems of reverse tutelage,
harnessing technological solutions, creating a critical technical
practice—when combined, shape the creation of these new types of political and
affective community. These ideas are relevant to the work of AI at its most
fundamental because, recalling the use-cases in section 3, AI is shaped by,
and shapes the evolution of contemporary political community.

Finally, these views of AI taken together lead us quickly towards fundamental
philosophical questions of what it is to be human—how we relate and live with
each other in spaces that are both physical and digital, how we navigate
difference and transcultural ethics, how we reposition the roles of culture
and power at work in daily life—and how the answers to these questions are
reflected in the AI systems we build. Here alone is there an ongoing need for
research and action, and to which historical hindsight and technological
foresight can make significant contributions.

##  5 Conclusions

This paper aims to provide a perspective on the importance of a critical
science approach, and in particular of decolonial thinking, in understanding
and shaping the ongoing advances in AI. Despite these fields having operated
mostly apart, our hope is that decolonial theories will expand the
practitioner’s ability, including ourselves, to ask critical questions of
technology research and design, to imagine alternative realities, to question
who is developing AI and where, and to examine the roles of culture and power
embedded in AI systems; reinvigorating Agre’s (1997) vision of a critical
technical practice of AI.

Operationalising this critical practice will require not only foresight and
case study research, but also approaches that support new research cultures,
along with innovative technical research in domains such as fairness, value
alignment, privacy, and interpretability. Moreover, there is a strong need to
develop new methodologies for inclusive dialogue between stakeholders in AI
development, particularly those in which marginalised groups have meaningful
avenues to influence the decision-making process, avoiding the potential for
predatory inclusion, and continued algorithmic oppression, exploitation, and
dispossession.

Any commitment to building the responsible and beneficial AI of the future
ties us to the hierarchies, philosophy and technology inherited from the past,
and a renewed responsibility to the technology of the present. To critically
engage with that inheritance, to avert algorithmic coloniality, to reveal the
relations of power that underlie technology deployment, to recover the
principles of living that were previously made incompatible with life, and to
create new forms of political and affective community, are the tasks for which
decolonial theories will provide key resources—whether these be through
heterogeneous practice, intercultural dialogue, creole AIs, reverse tutelage,
or a politics of friendship and love. The years ahead will usher in a wave of
new scientific breakthroughs and technologies driven by AI, making it
incumbent upon AI communities to strengthen the social contract through
ethical foresight and the multiplicity of intellectual perspectives available
to us, aligned with the goal of promoting beneficence and justice for all.

## Acknowledgements

We thank our reviewers and editor whose positive feedback and mentorship has
improved this paper. We are grateful to Desha Osborne, Irene Solaiman, Momin
Malik, and Vafa Ghazavi, who have influenced our thinking. We are also
grateful to many of our colleagues for their support, including Ben Coppin,
Brittany Smith, Courtney Biles, Dorothy Chou, Heiko Strathmann, Iason Gabriel,
James Besley, Kelly Clancy, Koray Kavukcuoglu, Laura Weidinger, Murray
Shanahan, and Sean Legassick.

Disclaimer: Any opinions presented in this paper represent the personal views
of the authors and do not necessarily reflect the official policies or
positions of their organisations.

## References

  * Agre (1997) Agre P (1997) Toward a critical technical practice: Lessons learned in trying to reform AI. In: Bowker G, Star S, Gasser L, Turner W (eds) Social Science, Technical Systems and Cooperative Work: Beyond the Great Divide, Psychology Press, pp 131–157
  * Agüera y Arcas et al. (2018) Agüera y Arcas B, Todorov A, Mitchell M (2018) Do algorithms reveal sexual orientation or just expose our stereotypes? Medium URL https://link.medium.com/GO7FJgFgM1
  * Ali (2016) Ali SM (2016) A brief introduction to decolonial computing. XRDS: Crossroads, The ACM Magazine for Students 22(4):16–21
  * AMA (2018) AMA (2018) Augmented intelligence in health care H-480.940. American Medical Association PolicyFinder
  * Amodei et al. (2016) Amodei D, Olah C, Steinhardt J, Christiano P, Schulman J, Mané D (2016) Concrete problems in AI safety. arXiv preprint arXiv:160606565
  * Angwin et al. (2016) Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. There’s software used across the country to predict future criminals. and it’s biased against blacks. ProPublica URL https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  * Arora (2019) Arora P (2019) Decolonizing privacy studies. Television & New Media 20(4):366–378
  * Ashcroft et al. (2006) Ashcroft B, Griffiths G, Tiffin H (2006) The post-colonial studies reader. Taylor & Francis
  * Asilomar Meeting (2017) Asilomar Meeting (2017) Asilomar AI principles. URL https://futureoflife.org/ai-principles/
  * Awori et al. (2016) Awori K, Bidwell NJ, Hussan TS, Gill S, Lindtner S (2016) Decolonising technology design. In: Proceedings of the First African Conference on Human Computer Interaction, pp 226–228
  * Balaram et al. (2018) Balaram B, Greenham T, Leonard J (2018) Artificial intelligence: Real public engagement. London: RSA URL https://www.thersa.org/discover/publications-and-articles/reports/artificial-intelligence-realpublic-engagement
  * Barocas and Selbst (2016) Barocas S, Selbst AD (2016) Big data’s disparate impact. Calif L Rev 104:671
  * Belmont Commission (1978) Belmont Commission (1978) The Belmont report: Ethical principles and guidelines for the protection of human subjects of research, vol 1. United States National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research
  * Benjamin (2019) Benjamin R (2019) Race after technology: Abolitionist tools for the new Jim Code. John Wiley & Sons
  * Bhambra et al. (2018) Bhambra GK, Nisancioglu K, Gebrial D (2018) Decolonizing the university. Pluto Press
  * Bhattacharyya et al. (2018) Bhattacharyya G, et al. (2018) Rethinking racial capitalism: Questions of reproduction and survival. Rowman & Littlefield International
  * Boden (2018) Boden MA (2018) Artificial intelligence: A very short introduction. Oxford University Press
  * Bonneuil (2000) Bonneuil C (2000) Development as experiment: Science and state building in late colonial and postcolonial africa, 1930-1970. Osiris 15:258–281
  * Brandt (1978) Brandt AM (1978) Racism and research: the case of the Tuskegee syphilis study. Hastings center report pp 21–29
  * Bruhn and Gallego (2012) Bruhn M, Gallego FA (2012) Good, bad, and ugly colonial activities: Do they matter for economic development? Review of economics and statistics 94(2):433–461
  * Bueter (2015) Bueter A (2015) The irreducibility of value-freedom to theory assessment. Studies in History and Philosophy of Science Part A 49:18–26
  * Bullington and Lane (2018) Bullington J, Lane E (2018) How a tech firm brought data and worry to new orleans crime fighting. The New Orleans Times-Picayune URL https://www.nola.com/news/crime_police/article_33b8bf05-722f-5163-9a0c-774aa69b6645.html
  * Buolamwini and Gebru (2018) Buolamwini J, Gebru T (2018) Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency, pp 77–91
  * Butorac (2018) Butorac SK (2018) Hannah Arendt, James Baldwin, and the politics of love. Political Research Quarterly 71(3):710–721
  * Canetti et al. (2019) Canetti R, Cohen A, Dikkala N, Ramnarayan G, Scheffler S, Smith A (2019) From soft classifiers to hard decisions: How fair can we be? In: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, pp 309–318
  * Capurro (2018) Capurro R (2018) Intercultural information ethics. In: Localizing the Internet, Wilhelm Fink Verlag, pp 19–38
  * Champion (2005) Champion T (2005) Metropole and margin: the dependency theory and the political economy of the Solomon Islands, 1880–1980. In: Centre and Periphery, Routledge, pp 43–60
  * Chen et al. (2019) Chen IY, Szolovits P, Ghassemi M (2019) Can AI help reduce disparities in general medical and mental health care? AMA journal of ethics 21(2):167–179
  * Chiappa and Isaac (2018) Chiappa S, Isaac WS (2018) A causal Bayesian networks viewpoint on fairness. In: Kosta E, Pierson J, Slamanig Dand Fischer-Hübner S, Krenn S (eds) Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data, IFIP Advances in Information and Communication Technology, Springer, Cham, vol 547
  * Coates et al. (1985) Coates JF, et al. (1985) Foresight in federal government policy making. Futures Research Quarterly 1(2):29–53
  * Costanza-Chock (2018) Costanza-Chock S (2018) Design justice, AI, and escape from the matrix of domination. Journal of Design and Science
  * Couldry and Mejias (2019a) Couldry N, Mejias UA (2019a) The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press
  * Couldry and Mejias (2019b) Couldry N, Mejias UA (2019b) Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media 20(4):336–349
  * Dastin (2018) Dastin J (2018) Amazon scraps secret AI recruiting tool that showed bias against women. San Fransico, CA: Reuters Retrieved on October 9:2018
  * Derrida (1993) Derrida J (1993) Politics of friendship. American Imago 50(3):353–391
  * D’Ignazio and Klein (2020) D’Ignazio C, Klein LF (2020) Data feminism. MIT Press
  * DiSalvo (2012) DiSalvo C (2012) Adversarial Design (Design Thinking, Design Theory) MIT Press. MIT Press
  * Douglas (2007) Douglas H (2007) Rejecting the ideal of value-free science. In: Kincaid H, Dupré J, Wylie A (eds) Value-Free Science: Ideals and Illusions?, Oxford University Press, Oxford, chap 6, pp 120–141
  * Dourish and Mainwaring (2012) Dourish P, Mainwaring SD (2012) Ubicomp’s colonial impulse. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, ACM, pp 133–142
  * Dourish et al. (2004) Dourish P, Finlay J, Sengers P, Wright P (2004) Reflective HCI: Towards a critical technical practice. In: Conference on Human Factors in Computing Systems: CHI’04 extended abstracts on Human factors in computing systems, 29, pp 1727–1728
  * DSTI (2019) DSTI (2019) Sierra leone national innovation & digital strategy 2019 - 2029. digitization for all: Identity, economy, and governance. URL https://www.dsti.gov.sl/wp-content/uploads/2019/11/Sierra-Leone-National-Innovation-and-Digital-Strategy.pdf
  * Dutton (2018) Dutton T (2018) An overview of national AI strategies. Medium URL https://link.medium.com/jqPZBjs7j2
  * Dwork et al. (2012) Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226
  * Edgerton (2007a) Edgerton D (2007a) Creole technologies and global histories: rethinking how things travel in space and time. Journal of History of Science and Technology 1(1):75–112
  * Edgerton (2007b) Edgerton DE (2007b) The contradictions of techno-nationalism and techno-globalism: A historical perspective. New Global Studies 1(1)
  * El Khayat and Khatibi (2010) El Khayat R, Khatibi A (2010) Open Correspondence: An Epistolary Dialogue. UNO Press, Translated by Babana-Hampton S, Orlando VK, Vogl M
  * Elliott and McKaughan (2014) Elliott KC, McKaughan DJ (2014) Nonepistemic values and the multiple goals of science. Philosophy of Science 81(1):1–21
  * Escobar (2011) Escobar A (2011) Sustainability: Design for the pluriverse. Development 54(2):137–140
  * Ess (2006) Ess C (2006) Ethical pluralism and global information ethics. Ethics and Information Technology 8(4):215–226
  * Eubanks (2018) Eubanks V (2018) Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press
  * Fanon (1961) Fanon F (1961) The wretched of the earth. Grove/Atlantic, Inc.
  * Fanon (1967) Fanon F (1967) Black skin, white masks. Grove press
  * Feenberg (2017) Feenberg A (2017) Critical theory of technology and STS. Thesis Eleven 138(1):3–12
  * Floridi (2019) Floridi L (2019) Translating principles into practices of digital ethics: five risks of being unethical. Philosophy & Technology 32(2):185–193
  * Floridi et al. (2018) Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, et al. (2018) AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines 28(4):689–707
  * Forsythe (2001) Forsythe D (2001) Studying those who study us: An anthropologist in the world of artificial intelligence. Stanford University Press
  * Forsythe (1993) Forsythe DE (1993) Engineering knowledge: The construction of knowledge in artificial intelligence. Social studies of science 23(3):445–477
  * Freudenthal (2019) Freudenthal E (2019) Ebola’s lost blood: row over samples flown out of africa as’ big pharma’set to cash in. The Telegraph
  * Friedman et al. (2013) Friedman B, Kahn PH, Borning A, Huldtgren A (2013) Value sensitive design and information systems. In: Early engagement and new technologies: Opening up the laboratory, Springer, pp 55–95
  * Gabriel (2020) Gabriel I (2020) Artificial intelligence, values and alignment. arXiv preprint arXiv:200109768
  * Gallopin (1992) Gallopin G (1992) Science, technology and the ecological future of Latin America. World Development 20(10):1391–1400
  * Gandhi (2006) Gandhi L (2006) Affective communities: Anticolonial thought, fin-de-siècle radicalism, and the politics of friendship. Duke University Press
  * Garcia (2019) Garcia E (2019) The militarization of artificial intelligence: a wake-up call for the global south. Available at SSRN 3452323
  * Gavighan et al. (2019) Gavighan C, Knott A, Maclaurin J, Zerilli J John Liddicoat (2019) Government use of artificial intelligence in New Zealand. Law Society, New Zealand
  * Gebru et al. (2018) Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daumé III H, Crawford K (2018) Datasheets for datasets. arXiv preprint arXiv:180309010
  * Gerrish (2018) Gerrish S (2018) How smart machines think. MIT Press
  * Gershgorn (2019) Gershgorn D (2019) Africa is building an AI industry that doesn’t look like Silicon Valley. Medium OneZero URL https://bit.ly/2SBnQFm
  * Gomberg-Muñoz (2018) Gomberg-Muñoz RM (2018) Review essay: Law and migrant labor in the 20th century: Ghost workers and global capitalism. PoLAR: Political and Legal Anthropology Review
  * Gopal (2019) Gopal P (2019) Insurgent Empire: Anticolonial Resistance and British Dissent. Verso Books
  * Goyanes (2018) Goyanes R (2018) Data for black lives is an organization using analytics as a tool for social change. Garage Magazine URL https://garage.vice.com/en_us/article/kzn4jn/data-for-black-lives-is-an-organization-using-analytics-as-a-tool-for-social-change
  * Gray and Suri (2019) Gray ML, Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books
  * Green (2019) Green B (2019) “Good” isn’t good enough. In: NeurIPS Workshop on AI for Social Good
  * Greene et al. (2019) Greene D, Hoffmann AL, Stark L (2019) Better, nicer, clearer, fairer: A critical assessment of the movement for ethical artificial intelligence and machine learning. In: Proceedings of the 52nd Hawaii International Conference on System Sciences
  * Hacking (2015) Hacking I (2015) Biopower and the avalanche of printed numbers. Biopower: Foucault and beyond pp 65–80
  * Hanna et al. (2019) Hanna A, Denton E, Smart A, Smith-Loud J (2019) Towards a critical race methodology in algorithmic fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
  * Hao (2019) Hao K (2019) An AI startup has found a new source of cheap labor for training algorithms: prisoners. MIT Tech Review URL https://www.technologyreview.com/f/613246/an-ai-startup-has-found-a-new-source-of-cheap-labor-for-training-algorithms/
  * Harding (2011) Harding S (2011) The postcolonial science and technology studies reader. Duke University Press
  * Harvey (2004) Harvey D (2004) The ‘new’ imperialism: Accumulation by dispossession. Socialist Register 40:63–87
  * Hogarth (2018) Hogarth I (2018) AI nationalisms. URL https://www.ianhogarth.com/blog/2018/6/13/ai-nationalism
  * IEEE Global Initiative (2016) IEEE Global Initiative (2016) Ethically aligned design. IEEE Standards v1
  * Irani et al. (2010) Irani L, Vertesi J, Dourish P, Philip K, Grinter RE (2010) Postcolonial computing: a lens on design and development. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 1311–1320
  * Isaac (2017) Isaac WS (2017) Hope, hype, and fear: the promise and potential pitfalls of artificial intelligence in criminal justice. Ohio St J Crim L 15:543
  * ITU (2019) ITU (2019) United nations activities on artificial intelligence (AI). International Telecommunication Union URL https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-UNACT-2019-1-PDF-E.pdf
  * James (1993) James CLR (1993) Beyond a boundary. Duke University Press
  * Jansen (2019) Jansen J (2019) Decolonisation in universities: The politics of knowledge. Wits University Press
  * Jasanoff and Hurlbut (2018) Jasanoff S, Hurlbut JB (2018) A global observatory for gene editing. Nature 555(7697):435–437
  * Jobin et al. (2019) Jobin A, Ienca M, Vayena E (2019) The global landscape of AI ethics guidelines. Nature Machine Intelligence 1(9):389–399
  * Johnston (2019) Johnston K (2019) A comparison of two smart cities: Singapore and Atlanta. Journal of Comparative Urban Law and Policy 3:191
  * Jung et al. (2019) Jung C, Kearns M, Neel S, Roth A, Stapleton L, Wu ZS (2019) Eliciting and enforcing subjective individual fairness. arXiv preprint arXiv:190510660
  * Kanth (2019) Kanth DR (2019) India boycotts ‘Osaka Track’ at G20 summit. Live Mint URL https://www.livemint.com/news/world/india-boycotts-osaka-track-at-g20-summit-1561897592466.html
  * Katell et al. (2020) Katell M, Young M, Dailey D, Herman B, Guetler V, Tam A, Binz C, Raz D, Krafft P (2020) Toward situated interventions for algorithmic equity: lessons from the field. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp 45–55
  * Keyes (2018) Keyes O (2018) The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on Human-Computer Interaction 2(CSCW):88
  * Kiros (1992) Kiros T (1992) Moral Philosophy and Development: The Human Condition in Africa, vol 61. Ohio Univ Ctr for International Studies
  * Latonero (2019) Latonero M (2019) Stop surveillance humanitarianism. New York Times URL https://www.nytimes.com/2019/07/11/opinion/data-humanitarian-aid.html
  * Laudan (1968) Laudan L (1968) Theories of scientific method from Plato to Mach: A bibliographical review. History of science 7(1):1–63
  * Law et al. (1987) Law J, et al. (1987) Technology and heterogeneous engineering: The case of Portuguese expansion. The social construction of technological systems: New directions in the sociology and history of technology 1:1–134
  * LeCun et al. (2015) LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
  * Lee (2017) Lee KF (2017) The real threat of artificial intelligence. The New York Times 24
  * Lee (2018) Lee KF (2018) AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt
  * Lum and Isaac (2016) Lum K, Isaac W (2016) To predict and serve? Significance 13(5):14–19
  * Madras et al. (2019) Madras D, Creager E, Pitassi T, Zemel R (2019) Fairness through causal awareness: Learning causal latent-variable models for biased data. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, pp 349–358
  * Maldonado-Torres (2007) Maldonado-Torres N (2007) On the coloniality of being: Contributions to the development of a concept. Cultural studies 21(2-3):240–270
  * Marda and Narayan (2020) Marda V, Narayan S (2020) Data in new delhi’s predictive policing system. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp 317–324
  * McClintock (1992) McClintock A (1992) The angel of progress: pitfalls of the term “post-colonialism”. Social text (31/32):84–98
  * McDowell and Chinchilla (2016) McDowell C, Chinchilla MY (2016) 30 partnering with communities and institutions. Civic Media: Technology, Design, Practice p 461
  * Mezzadra and Neilson (2017) Mezzadra S, Neilson B (2017) On the multiple frontiers of extraction: excavating contemporary capitalism. Cultural Studies 31(2-3):185–204
  * Mignolo (2007) Mignolo WD (2007) Introduction: Coloniality of power and de-colonial thinking. Cultural studies 21(2-3):155–167
  * Mignolo (2012) Mignolo WD (2012) Local histories/global designs: Coloniality, subaltern knowledges, and border thinking. Princeton University Press
  * Mikesell et al. (2013) Mikesell L, Bromley E, Khodyakov D (2013) Ethical community-engaged research: a literature review. American journal of public health 103(12):e7–e14
  * Milan (2013) Milan S (2013) Social movements and their technologies: Wiring social change. Springer
  * Milan and Treré (2019) Milan S, Treré E (2019) Big data from the south (s): Beyond data universalism. Television & New Media 20(4):319–335
  * Milan and Van der Velden (2016) Milan S, Van der Velden L (2016) The alternative epistemologies of data activism. Digital Culture & Society 2(2):57–74
  * Mitchell et al. (2019) Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, Spitzer E, Raji ID, Gebru T (2019) Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, pp 220–229
  * Mitchell et al. (2018) Mitchell S, Potash E, Barocas S, D’Amour A, Lum K (2018) Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:181107867
  * Mitchell (2006) Mitchell TM (2006) The discipline of machine learning. Tech. Rep. CMU-ML-06-108, Machine Learning Department, Carnegie Mellon University
  * Nabi and Shpitser (2018) Nabi R, Shpitser I (2018) Fair inference on outcomes. In: Thirty-Second AAAI Conference on Artificial Intelligence
  * Nandy (1989) Nandy A (1989) Intimate enemy: Loss and recovery of self under colonialism. Oxford University Press Oxford
  * Ndlovu-Gatsheni (2015) Ndlovu-Gatsheni SJ (2015) Decoloniality as the future of Africa. History Compass 13(10):485–496
  * Nelson (2002) Nelson A (2002) Unequal treatment: Confronting racial and ethnic disparities in health care. Journal of the National Medical Association 94(8):666
  * Nissenbaum (2001) Nissenbaum H (2001) How computer systems embody values. Computer 34(3):120–119
  * Noble (2018) Noble SU (2018) Algorithms of oppression: How search engines reinforce racism. NYU Press
  * Nuremberg Military Tribunals (1947) Nuremberg Military Tribunals (1947) Permissible medical experiments. In: Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10, vol 2, U.S. Government Printing Office, pp 181–182
  * Nyabola (2018) Nyabola N (2018) Digital democracy, analogue politics: How the Internet era is transforming politics in Kenya. Zed Books Ltd.
  * Nyawa (2019) Nyawa JM (2019) The big brother is watching: Huduma Namba a threat to our rights and freedoms. Available at SSRN 3389268
  * Obermeyer et al. (2019) Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453
  * OECD (2019) OECD (2019) OECD principles on artificial intelligence. URL https://www.oecd.org/going-digital/ai/principles/
  * O’Neil (2016) O’Neil C (2016) Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books
  * Pappas (2017) Pappas GF (2017) The limitations and dangers of decolonial philosophies: Lessons from Zapatista Luis Villoro. Radical Philosophy Review
  * Pathways for Prosperity (2019) Pathways for Prosperity (2019) Digital diplomacy: Technology governance for developing countries. Pathways for Prosperity Commission on Technology and Inclusive Development URL https://pathwayscommission.bsg.ox.ac.uk/sites/default/files/2019-10/Digital-Diplomacy.pdf
  * Philip et al. (2012) Philip K, Irani L, Dourish P (2012) Postcolonial computing: A tactical survey. Science, Technology, & Human Values 37(1):3–29
  * Van de Poel and Kroes (2014) Van de Poel I, Kroes P (2014) Can technology embody values? In: The moral status of technical artefacts, Springer, pp 103–124
  * Pollard et al. (2011) Pollard J, McEwan C, Hughes A (2011) Postcolonial economies. Zed Books London/New York
  * Quijano (2000) Quijano A (2000) Coloniality of power and Eurocentrism in Latin Americas. International Sociology 15(2):215–232
  * Quijano (2007) Quijano A (2007) Coloniality and modernity/rationality. Cultural studies 21(2-3):168–178
  * Raji and Dobbe (2020) Raji ID, Dobbe R (2020) Concrete problems in AI safety, revisited. In: ICLR Workshop on ML in the Real World
  * Ramose (1999) Ramose MB (1999) African philosophy through Ubuntu. Mond Books
  * Ricaurte (2019) Ricaurte P (2019) Data epistemologies, the coloniality of power, and resistance. Television & New Media 20(4):350–365
  * Richardson et al. (2019) Richardson R, Schultz J, Crawford K (2019) Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online, Forthcoming
  * Rock and Grant (2016) Rock D, Grant H (2016) Why diverse teams are smarter. Harvard Business Review 4(4):2–5
  * Ronconi (2015) Ronconi L (2015) Enforcement and the effective regulation of labor. Tech. rep., IDB Working Paper Series
  * Røpke (2001) Røpke I (2001) New technology in everyday life–social processes and environmental impact. Ecological economics 38(3):403–422
  * Russell and Norvig (2016) Russell S, Norvig P (2016) Artificial intelligence: a modern approach. Prentice Hall
  * Said (1993) Said EW (1993) Culture and imperialism. Vintage
  * Sap et al. (2019) Sap M, Card D, Gabriel S, Choi Y, Smith NA (2019) The risk of racial bias in hate speech detection. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 1668–1678
  * Schroeder et al. (2018) Schroeder D, Cook Lucas J, Hirsch F, Fenet S, Muthuswamy V (2018) Ethics dumping case studies from north-south research collaborations. Springer International Publishing
  * Selbst et al. (2019) Selbst AD, Boyd D, Friedler SA, Venkatasubramanian S, Vertesi J (2019) Fairness and abstraction in sociotechnical systems. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, pp 59–68
  * Sengers et al. (2005) Sengers P, Boehner K, David S, Kaye J (2005) Reflective design. In: Proceedings of the 4th decennial conference on Critical computing: Between sense and sensibility, pp 49–58
  * Sengers et al. (2006) Sengers P, McCarthy J, Dourish P (2006) Reflective HCI: Articulating an agenda for critical practice. In: CHI’06 extended abstracts on Human factors in computing systems, ACM, pp 1683–1686
  * Senior (2018) Senior E (2018) The Caribbean and the medical imagination, 1764-1834: Slavery, disease and colonial modernity, vol 119. Cambridge University Press
  * Shore (2006) Shore N (2006) Re-conceptualizing the Belmont report: A community-based participatory research perspective. Journal of Community Practice 14(4):5–26
  * Siddiqui and Singh (2015) Siddiqui AU, Singh HK (2015) “Aadhar” management system. IITM Journal of Management and IT 6(1):40–43
  * Sokoloff and Pincus (2008) Sokoloff NJ, Pincus FL (2008) Introduction: Race, class, gender, and capitalism. Race, Gender & Class pp 4–8
  * Stark (2019) Stark L (2019) Facial recognition is the plutonium of AI. XRDS: Crossroads, The ACM Magazine for Students 25(3):50–55
  * Steiner (1994) Steiner CB (1994) Technologies of resistance: Structural alteration of trade cloth in four societies. Zeitschrift für Ethnologie pp 75–94
  * Stoler (2008) Stoler AL (2008) Epistemic politics: Ontologies of colonial common sense. In: The Philosophical Forum, Wiley Online Library, vol 39, pp 349–361
  * Taylor (2016) Taylor E (2016) Groups and oppression. Hypatia 31(3):520–536
  * Thatcher et al. (2016) Thatcher J, O’Sullivan D, Mahmoudi D (2016) Data colonialism through accumulation by dispossession: New metaphors for daily data. Environment and Planning D: Society and Space 34(6):990–1006
  * Thrush (2008) Thrush C (2008) American curiosity: Cultures of natural history in the colonial British Atlantic world. Environmental History 13(3):573
  * Tilley (2014) Tilley H (2014) Conclusion: Experimentation in colonial east africa and beyond. The International Journal of African Historical Studies 47(3):495–505
  * Tomašev et al. (2020) Tomašev N, Cornebise J, Hutter F, Mohamed S, Picciariello A, Connelly B, Belgrave DC, Ezer D, van der Haert FC, Mugisha F, Abila G, Arai H, Almiraat H, Proskurnia J, Snyder K, Otake-Matsuura M, Othman M, Glasmachers T, de Wever W, Teh YW, Khan ME, De Winne R, Schaul T, Clopath C (2020) Ai for social good: unlocking the opportunity for positive impact. Nature Communications 11(1):1–6
  * Toyama (2015) Toyama K (2015) Geek heresy: Rescuing social change from the cult of technology. PublicAffairs
  * UK National Health Service (2019) UK National Health Service (2019) Code of conduct for data-driven health and care technology
  * UNCTAD (2013) UNCTAD (2013) Information economy report 2013: The cloud economy and developing countries. United Nations Conference on Trade and Development
  * Vaithianathan et al. (2013) Vaithianathan R, Maloney T, Putnam-Hornstein E, Jiang N (2013) Children in the public benefit system at risk of maltreatment: Identification via predictive modeling. American journal of preventive medicine 45(3):354–359
  * Vinuesa et al. (2020) Vinuesa R, Azizpour H, Leite I, Balaam M, Dignum V, Domisch S, Felländer A, Langhans SD, Tegmark M, Nerini FF (2020) The role of artificial intelligence in achieving the sustainable development goals. Nature Communications 11(1):1–10
  * Vitak et al. (2016) Vitak J, Shilton K, Ashktorab Z (2016) Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, ACM, pp 941–953
  * Wa Thiong’o (1992) Wa Thiong’o N (1992) Decolonising the mind: The politics of language in African literature. East African Publishers
  * Wallerstein (1987) Wallerstein I (1987) World-systems analysis
  * Washington (2006) Washington HA (2006) Medical apartheid: The dark history of medical experimentation on Black Americans from colonial times to the present. Doubleday Books
  * Whittlestone et al. (2019) Whittlestone J, Nyrup R, Alexandrova A, Dihal K, Cave S (2019) Ethical and societal implications of algorithms, data, and artificial intelligence: a roadmap for research. London: Nuffield Foundation
  * Winner (1980) Winner L (1980) Do artifacts have politics? Daedalus pp 121–136
  * WMA General Assembly (1964) WMA General Assembly (1964) Ethical principles for medical research involving human subjects. World Medical Association Declaration of Helsinki URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566407/pdf/11357217.pdf
  * Wong (2012) Wong PH (2012) Dao, harmony and personhood: Towards a Confucian ethics of technology. Philosophy & technology 25(1):67–86
  * Young et al. (2019) Young M, Magassa L, Friedman B (2019) Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents. Ethics and Information Technology 21(2):89–103
  * Yuan (2018) Yuan L (2018) How cheap labor drives china’s A.I. ambitions. New York Times URL https://www.nytimes.com/2018/11/25/business/china-artificial-intelligence-labeling.html
  * Zembylas (2017) Zembylas M (2017) Love as ethico-political practice: Inventing reparative pedagogies of aimance in “disjointed” times. Journal of curriculum and pedagogy 14(1):23–38
  * Zuboff (2019) Zuboff S (2019) The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books