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Re-imagining Algorithmic Fairness in India and Beyond
Open accessAbstract
Conventional algorithmic fairness is West-centric, as seen in its subgroups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. Based on 36 qualitative interviews and a discourse analysis of algorithmic deployments in India, we find that several assumptions of algorithmic fairness are challenged. We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration. We contend that localising model fairness alone can be window dressing in India, where the distance between models and oppressed communities is large. Instead, we re-imagine algorithmic fairness in India and provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper de-centers West-centric algorithmic fairness by analyzing AI power in India through 36 expert interviews and discourse analysis, arguing that core fairness assumptions about reliable data, unbiased ML makers, and accountable ecosystems fail in the Indian context. It proposes a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems rather than only localising model fairness.
Key findings
- Data and model distortions: datasets are not always reliable due to socio-economic factors; half the Indian population lacks Internet access, with the excluded half primarily women, rural communities, and Adivasis; 67% of Internet users are male; models overfit digitally-rich profiles, typically middle-class men; caste, gender, and religion require different fairness implementations.
- Double standards and distance by ML makers: Indian users are perceived as “bottom billion” data subjects and “petri dishes” for intrusive models, with poor recourse; AI makers are described as distant and largely privileged class/caste males, e.g., Dalit sanitation workers tracked with microphones, GPS, cameras, and SIMs.
- Unquestioning AI aspiration: AI is aspirational in the Indian state, media, and legislation and is adopted early in high-stakes domains, while a weak ecosystem of journalists, researchers, and activists inhibits meaningful fairness; of 138 FAccT papers in 2019–2020, only one substantially engaged a non-Western context—Marda’s paper on New Delhi’s predictive policing.
- Proposed roadmap: re-contextualise data and models, empower oppressed communities through participatory action, and enable Fair-ML ecosystems; reservations are discussed as an Indic justice approach, with quotas ranging from 30% to 80% depending on policy.
Methods
- Qualitative interviews with 36 expert researchers, activists, and lawyers working closely with marginalised Indian communities: Computer Science (11), Activism (9), Law and Public Policy (6), Science and Technology Studies (5), Development Economics (2), Sociology (2), and Journalism (1); all had 10–30 years of experience; 24 were based in India, 2 in Europe, 1 in Southeast Asia, and the rest in the USA; 25 self-identified as male, 10 as female, and 1 as non-binary.
- Semi-structured one-hour interviews in English via video conferencing, captured through field notes and video recordings; recruitment used purposeful sampling until saturation; respondents were compensated except government employees; transcripts were inductively coded with seven top-level categories and analysed using feminist, decolonial, and anti-caste lenses.
- Discourse analysis of algorithmic deployments and emerging policies in India starting from Aadhaar (2009), including Indian news publications, policy documents, community media, and prior research.
Limitations
- Stated: the authors acknowledge that their interpretations of research ethics may not be universal, and that all authors come from privileged positions of class and/or caste; three authors are Indian and two are White.
- Stated: the concerns raised may also be true of other countries, and the broader goal should be global approaches to Fair-ML; the authors describe the challenges as large and open-ended.
- Evident from the text: the sample consists of 36 experts rather than directly affected community members, interviews were conducted in English, recruitment was purposive, 25 of 36 respondents were male, and the discourse analysis relies on secondary sources; findings are qualitative and interpretive rather than statistically generalizable.
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81 Not checked yet · 39 Own result · 23 No basis given · 21 Own interpretation · 1 Own method
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In the paper
“Proxies may not generalise even within the country, e.g., asset ownership: “If you live in Mumbai, having a motorbike is a nuisance.If rural, you're the richest in town.”(P9, CS/IS researcher).”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
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In the paper
“Data and model distortions: Infrastructures and social contracts in India challenge the assumption that datasets are faithful representations of people and phenomena.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
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In the paper
“We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Abstract
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In the paper
“Ground truth on full names, location, contact details, biometrics, and their usage patterns can be unstable, especially for marginalised groups.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Mis-recorded identities
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In the paper
“Data was reported to be 'missing' due to artful user practices to manipulate algorithms, motivated by privacy, abuse, and reputation concerns.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
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In the paper
“However the one-to-one correspondence in datasets often fails in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Mis-recorded identities
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In the paper
“Many discussed how consent to a data worker stemmed from high interpersonal trust and holding them in high respect-relationships which may not be transitive to the downstream AI applications.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
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In the paper
“A few respondents pointed to how household dynamics impacted data collection, especially when using the door-to-door data collection method, e.g., how heads of households, typically men, often answered data-gathering surveys on behalf of women, but responses were recorded as women's.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
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In the paper
“We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Abstract
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In the paper
“However, this approach fails due to device sharing patterns [178], increased use of multiple SIM cards, and the frequency with which people change their numbers.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [178]
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In the paper
“User identity can be mis-recorded by the data collection instrument, assuming a static individual correspondence or expected behaviour.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Mis-recorded identities
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In the paper
“We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Abstract
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“Several respondents mentioned how location may not be permanent or tethered to a home, e.g., migrant workers regularly travel across porous nation-state boundaries.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
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In the paper
“Many respondents pointed to the frequent unavailability of socioeconomic and demographic datasets at national, state, and municipal levels for public fairness research.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
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In the paper
“Further, the high-tech illegibility of AI was noted to render recourse out of reach for groups marginalised by literacy, legal, and educational capital (see [208] for 'hiding behind a computer').”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [208]
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In the paper
“Respondents described how AI models in India overfitted to 'good' data profiles of the digitally-rich, privileged communities, as a result of poor cultural understanding and exclusion on part of AI makers.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
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In the paper
“Many respondents shared how street-level bureaucrats, administrative offices, and front line workers-the human infrastructures [182] who played a crucial role in providing recourse to marginalised Indian communities-were removed in AI systems.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [182]
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In the paper
“Model re-training left new room for bias, though, due to a lack of Fair-ML standards for India,”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
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In the paper
“The AI embrace was attributed to follow the trajectory of recent high-tech interventions (such as Aadhaar, MGNREGA payments, and the National Register of Citizens (NRC)).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
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In the paper
“Several respondents described how lack of recourse was even more dire for marginalised users.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
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In the paper
“Several respondents discussed how AI developers, both regional and international-private and statetreated Indian user communities as 'petri dishes' for models.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
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In the paper
“Popular lending apps equate 'good credit' with whether the user called their parents daily, had stored over 58 contacts, played car-racing games, and could repay in 30 days [56].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [56]
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“Model inputs in India appear to be disproportionately skewed due to large disparities in digital access.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
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In the paper
“Several respondents described how AI makers had a transactional mindset towards Indians, seeing them as agency-less data subjects that generated large-scale behavioural traces to improve models.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
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In the paper
“Respondents noted that the sub-groups that had access to underlying variables for data-rich profiles, like money, mobility, and literacy, were typically middle-class men.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
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In the paper
“Nearly all respondents described how AI makers and researchers, including regional makers, were heavily distant from the Indian communities they served.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
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“Respondents described that when recourse existed, it was often culturally-insensitive or dehumanising, e.g., a respondent was violently questioned about their clothing by staff of a ride-sharing application, during redressal for an assault faced in a taxi (also see [177] for poor recourse).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [177] source
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In the paper
“Many respondents described how public sector AI projects in India were viewed as modernising efforts to overcome age-old inefficiencies in resource delivery (also in [175]).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [175] source
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In the paper
“Nearly all respondents discussed reservations as a technique for algorithmic fairness in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
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In the paper
“Respondents described how technology journalists in India-a keystone species for public discourse-covered app launches and investments, and less on algorithms and their impacts.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
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In the paper
“Many respondents pointed to how emerging '4good' deployments tended to leave out minorities.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Disputing AI4All
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In the paper
“Issues of algorithmic bias were not widely raised in the public consciousness in India, at the time of the study.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
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In the paper
“In contrast to the 'black box AI problem', i.e., even the humans who design models do not always understand how variables are being combined to make inferences [171], many respondents discussed an end-to-end opacity of inputs, model behaviour, and outcomes in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
What the sources say
- Not checked yetcites [171]
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In the paper
“In Section 4, we demonstrated that several underlying assumptions about algorithmic fairness and its enabling infrastructures fail in the Indian context.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5 TOWARDS AI FAIRNESS IN INDIA
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- Rests on an assumption: The views and experiences reported by the interview respondents are representative enough of Indian AI practices to support general conclusions about India.
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In the paper
“Several respondents discussed a lack of inclusion of diverse stakeholders in decision-making processes, laws, and policies for public sector AI.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
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In the paper
“Conventional algorithmic fairness is West-centric, as seen in its subgroups, values, and methods.”
- Also stated in: § 1 INTRODUCTION · plain
“Despite the exponential growth of fairness in Machine Learning (AI) research, it remains centred on Western concerns and histories-the structural injustices (e.g., race and gender), the data (e.g., ImageNet), the measurement scales (e.g., Fitzpatrick scale), the legal tenets (e.g., equal opportunity), and the enlightenment values.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Abstract
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- Also stated in: § 1 INTRODUCTION · plain
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In the paper
“Fairness' status as the de facto moral standard of choice and signifier of justice, is itself a sign of cultural situatedness.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.3 Philosophical roots.
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In the paper
“Conventional measurements of algorithmic fairness make several assumptions based on Western institutions and infrastructures.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
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In the paper
“We argue that the above assumptions may not hold in much else of the world.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
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In the paper
“The community also often repurposes terminology from US legal domains, such as “disparate impact”, “disparate treatment”, and “equal opportunity”, or use them as points of triangulation, in order to compare technical properties of fairness through analogy with legal concepts [81,82].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.2 Legal framing.
What the sources say
- Not checked yetcites [81] source
- Not checked yetcites [82]
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In the paper
“While other axes of discrimination and injustices such as disability status [91], age [59], and sexual orientation [76] have gotten some attention, biases relevant to other geographies and cultures are not explored (e.g., Adivasis (indigeneous tribes of South Asia) and Dalits).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.1 Axes of discrimination.
What the sources say
- Not checked yetcites [91]
- Not checked yetcites [59]
- Not checked yetcites [76]
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In the paper
“The above works point to the dangers in defining fairness of algorithmic systems based solely on a Western lens.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.2 Fairness perceptions across cultures
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While algorithmic fairness keeps AI within ethical and legal boundaries in the West, there is a real danger that naive generalisation of fairness will fail to keep AI deployments in check in the non-West.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Even the categorisation of proxies in fairness analyses have Western biases and origins; e.g., the Fitzpatrick skin type is often used by researchers as a phenotype [46], but was originally developed to categorise UV sensitivity) [73].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.1 Axes of discrimination.
What the sources say
- Not checked yetcites [46]
- Not checked yetcites [73] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Furthermore, cultural relationships such as power distance, and temporal orientation, are known to mediate the importance placed on fairness [122].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.3 Philosophical roots.
What the sources say
- Not checked yetcites [122]
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Connections have been made between algorithmic fairness and Western concepts such as egalitarianism [38], consequentialism [142,167], deontic justice [38,142], and Rawls' distributive justice [99,142].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.3 Philosophical roots.
What the sources say
- Not checked yetcites [38]
- Not checked yetcites [142]
- Not checked yetcites [167]
- Not checked yetcites [99]
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The call for a global lens in AI accountability is not new [84,155], but the ethical principles in AI are often interpreted, prioritised, contextualised, and implemented differently across the globe [98].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.3 Algorithmic fairness in the non-West
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since early inquiries into algorithmic fairness largely dealt with US law enforcement (predictive policing and recidivism risk assessment) as well as state regulations (e.g., in housing, loans, and education), the research framings often rely implicitly on US laws such as the Civil Rights Acts and Fair Housing Act, as well as on US legal concepts of discrimination.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.2 Legal framing.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“However these categories are culturally and historically situated [85].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.1 Axes of discrimination.
What the sources say
- Not checked yetcites [85] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“Traditional societies often value restorative justice, which emphasises repairing harms [44], rather than fairness, e.g., contemporary Australian Aboriginal leaders emphasise reconciliation rather than fairness in their political goals [94].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.3 Philosophical roots.
What the sources say
- Not checked yetcites [44]
- Not checked yetcites [94] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The majority of fairness research looks at racial [46,57,121,125,183] and gender biases [42,46,197,219] in models-two dimensions that dominate the American public discourse.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.1 Axes of discrimination.
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In some cases, though, data workers have been shown to fudge data without actual conversations with affected people; efforts like jun sanwais (public hearings) and Janata Information Systems organized by the Mazdoor Kisan Shakti Sangatan are examples to secure representation through data.[97,193].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
What the sources say
- Not checked yetcites [97] source
- Not checked yetcites [193]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“[12] shows that NLP models disproportionately fail to even detect names of people from non-Western backgrounds.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.3 Algorithmic fairness in the non-West
What the sources say
- Not checked yetcites [12]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Datasets derived from Internet connectivity will exclude a significant population, e.g., many argued that India's mandatory covid-19 contact tracing app excluded hundreds of millions due to access constraints, pointing to the futility of digital nation-wide tracing (also see [112]).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
What the sources say
- Not checked yetcites [112]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Sub-groups like caste (endogamous, ranked social identities, assigned at birth [17,191]), 1 gender, and religion require different fairness implementations; but AI systems in India are under-analyzed for biases, mirroring the limited mainstream public discourse on oppression.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
What the sources say
- Not checked yetcites [17]
- Not checked yetcites [191]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These barriers become more complicated when technology spans cultures: more, and more remote, hands are involved; intended behaviours may not be defined; computerblaming may meet computer-worship head on (see Section 4); and questions of ownership and liability become more complicated.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.4 Accountability for unfairness
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
In the paper
“Scholarship like Bond's caste report [184] argues that there is limited political will to collect and share socio-economic indicators by caste or religion.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
What the sources say
- Not checked yetcites [184]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We contend that localising model fairness alone can be window dressing in India, where the distance between models and oppressed communities is large.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, we find that conventional Fair-ML may be inappropriate, insufficient, or even inimical in India if it does not engage with the local structures.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“For instance, [190] highlights the glaring gaps in geo-diversity of open datasets such as ImageNet and Open Images that drive much of the computer vision research.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.3 Algorithmic fairness in the non-West
What the sources say
- Not checked yetcites [190] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In an international AI perceptions survey (2019), Indians ranked first in rating AI as 'exciting', 'futuristic' and 'mostly good for society' [110].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [110]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“Several studies have pointed to the successful redistribution of resources towards oppressed sub-groups, through reservations [43,68,152].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [43]
- Not checked yetcites [68]
- Not checked yetcites [152]
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“India's own justice approaches present new and relevant ways to operationalise algorithmic fairness locally.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Popular fairness techniques, such as equal opportunity and equal odds, stem from epistemological and legal systems of the US (e.g., [62,216]).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [62]
- Not checked yetcites [216]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Inscrutability suppresses algorithmic fairness.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.3.2 Critical transparency.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is important to note that operationalising fairness approaches from the West to these axes is often nontrivial.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.1.2 Model and model (un)fairness considerations.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A seminal report by Newslaundry/Oxfam described how privileged castes comprised Indian news media, invisibilising the vast oppressed majority in public discourse [145].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
What the sources say
- Not checked yetcites [145]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Similarly, unequal standards, inadequate safeguards, and dubious applications of AI in the non-West can lead to catastrophic effects (similar analogies have been made for content moderation [165,177]).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.3 First-world care in deployments.
What the sources say
- Not checked yetcites [165]
- Not checked yetcites [177] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“As an illustration, compared to the US undergraduate admission process of selection from a pool of candidates, the undergraduate admissions in India is done through various joint seat allocation processes, over hundreds of programmes, across dozens of universities and colleges that takes into account test scores, ordered preference lists provided by students, as well as various affirmative action”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.1.2 Model and model (un)fairness considerations.
What the sources say
- Not checked yetcites [31] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Past disasters in India, like the fatal Union Carbide gas leak in 1984-one of the world's worst industrial accidents-point to faulty design and low quality, double standards for the 'third world' [206].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.3 First-world care in deployments.
What the sources say
- Not checked yetcites [206] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Context-free assumptions in Fair-ML, whether in homegrown or international AI systems, can not just fail, but produce harm inadvertently when applied to different infrastructural and cultural systems.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.1 Participatory Fair-ML knowledge systems.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While algorithms may not be trained on sub-group identification, proxies may correspond to Dalits, Adivasis, and Muslims disproportionately.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI's 'neutral' and 'human-free' associations lent credence to its algorithmic authority.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In January 2020, over a thousand protestors were arrested during protests in Delhi, aided by FR.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
What the sources say
- Not checked yetcites [5]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 56
- Probably not a substantial claim. 51
In the paper
“of the 138 papers published in 2019 and 2020, only a handful of papers even mention non-West countries, and only one of them-Marda's paper on New Delhi's predictive policing system [127]-substantially engages with a non-Western context.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
What the sources say
- Not checked yetcites [127]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“• Occupation: traditional occupations may correspond to caste, gender, or religion; e.g., manual scavenging or butchery.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many respondents were concerned that the safety apps were populated by middle-class users and tended to mark Dalit, Muslim, and slum areas as unsafe, potentially leading to hyper-patrolling in these areas.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Double standards and distance by ML makers: Indian users are perceived as 'bottom billion' data subjects, petri dishes for intrusive models, and given poor recourse-thus, effectively limiting their agency.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“• Skin tone: may indicate caste, ethnicity, and class; however, unlike correlations between race and skin tone, correspondences to Indian sub-groups is weaker.Dark skin tones can be discriminated against in India [60].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [60]
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While Indians are part of the AI workforce, a majority work in services, and engineers do not entirely represent marginalities, limiting re-mediation of distances.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI is readily adopted in high-stakes domains, often too early.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Sub-groups like caste (endogamous, ranked social identities, assigned at birth [17,191]), 1 gender, and religion require different fairness implementations; but AI systems in India are under-analyzed for biases, mirroring the limited mainstream public discourse on oppression.”
- Also stated in: § Gender Identity & Sexual Orientation (No official · plain
“AI systems in India remain under-analysed for biases, mirroring the limited public discourse on oppression.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
What the sources say
- Not checked yetcites [17]
- Not checked yetcites [191]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Gender Identity & Sexual Orientation (No official · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The AI imaginary is aspirational in the Indian state, media, and legislation.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“• Zip codes: can correspond to caste, religion, ethnicity, or class. Many Indian zip codes are heterogeneous, with slums and posh houses abutting in the same area [20,36], unlike the rather homogeneous zip codes in the West from the segregated past [50].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [20]
- Not checked yetcites [36]
- Not checked yetcites [50]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some respondents reported on how the state and industry apparati collected and retained valuable, large-scale data, but the datasets were not always made publicly available due to infrastructure and non-transparency issues.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“• Expenditure: on dietary and lifestyle items may be proxies for religion, caste, or ethnicity; e.g., expenses on pork or beef.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Models are over-fitted for digitally-rich profiles-typically middle-class men-further excluding the 50% without Internet access.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many respondents observed a bias towards upper-middle class problems, data, and deployments due to easier data access and revenue, as P8, CS/IS (computer science/information sciences researcher) put it, “rich people problems like cardiac disease and cancer, not poor people's Tuberculosis, prioritised in AI [in India]”, exacerbating inequities among those who benefit from AI and those who do not.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many respondents described how datasets under-collected darker skin tones and measurement scales like Fitzpatrick scale are not calibrated on diverse Indian skin tones.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“• Names: are revelatory proxies for caste, religion, gender, and ethnicity, and have contributed to discrimination in India [15,202], e.g., Banerjee or Khan connote to caste or religion location.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [15]
- Not checked yetcites [202]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some respondents pointed to how Dalit and Muslim bodies were used as test subjects for AI surveillance, e.g., pointing to how human efficiency trackers were increasingly deployed among Dalit sanitation workers in cities like Panchkula and Nagpur.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several respondents described how strong aspiration for AI for socio-economic upliftment was accompanied by high trust in automation, limited transparency, and the lack of an empowered Fair-ML ecosystem in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast to how AI industry and research were moderately responsive to user bias reports in the West, recourse and redress for Indians were perceived to be non-existent.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Many described how social audits and working with civil society created a better understanding and accountability.8”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Many respondents described how lending models imagined middle-class men as end-users-even with many microfinance studies showing that women have high loan repayment rates [69,198].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [69]
- Not checked yetcites [198] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several respondents pointed to how automation solutions had fervent rhetoric; whereas in practice, accuracy and performance of systems were low.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Such interventions were criticised for placing power in the hands of dominant-caste supervisors.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many discussed how low quality designs, algorithms, and support were deployed for Indians, attributing to weak tech policy and enforcement of accountability in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“• Language: Language can correspond to religion, class, ethnicity, and caste.Many AI systems serve in English, which only 10% of Indians understand [174].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [174] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Many respondents described how the Indian technology sector claimed to be 'merit-based', open to anyone highly gifted in the technical sciences; but many have pointed to how merit is a function of caste privilege [196,207].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [196] source
- Not checked yetcites [207]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several respondents discussed how Indian AI engineers were largely”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“• Documentation: Several AI applications require state-issued documentation like Aadhaar or birth certificates, e.g., in finance.The economically poor are also reported to be document-poor [173].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [173]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“• Mobility: can correlate to gender and disability.Indian women travel shorter distances and depend on public transport, for safety and cost [138].Persons with disabilities often have lower mobility in India, due to a lack of ramps and accessible infrastructure [188].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [138]
- Not checked yetcites [188]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Many criticised how neo-liberal AI (including those from the state) tended to treat Indians as 'bottom billion data subjects' in the periphery [211]-being subject to intrusive models, non-consensual automation, poor tech policies, inadequate user research, low-cost or free products that are low standard, and considered 'unsaturated markets'.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [211]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In some cases, those with 'poor' data profiles subverted model predictions-as in P23's (STS researcher) research on financial lending, where women overwhelmingly availed and paid back loans in the names of male relatives to avoid perceived gender bias in credit scoring.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even when feedback mechanisms were included, respondents shared that they were not always localised for India, and incidents were not always recognised unless an activist contacted the company staff directly.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“• Devices and infrastructure: Internet access corresponds to gender, caste, and class, with 67% Internet users being males [102].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [102]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For instance, P11, tech policy researcher, illustrated how lending apps instantly determined creditworthiness through alternative credit histories built based on the user's SMS messages, calls, and social networks (due to limited credit or banking history).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some talked about a colonial mindset of tight control in decision-making on automation laws, leading to reticent and monoscopic views by the judiciary and state.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“e.g., P29 (LGBTQ+ activist) discussed how AI was justified in the public domain, e.g., surveillance for smart cities, 11 as women's safety measures, but tended to invisibilise transgender members or increase monitoring of Dalit and Muslim areas, e.g., a FR was deployed outside women's restrooms to detect intrusion by non-female entrants, potentially leading to violence against transgender members.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Disputing AI4All
hedgeddirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Many respondents expressed concern over AI advances in detecting sexuality, criminality, or terrorism (e.g., [187,212]) potentially being exported to India and harming minorities.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Disputing AI4All
What the sources say
- Not checked yetcites [187]
- Not checked yetcites [212] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Critiques were raised in our study on how neo-liberal AI followed a broader pattern of extraction from the 'bottom billion' data subjects and labourers.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.3 First-world care in deployments.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Many described how disproportionate confidence in high-tech solutions, combined with limited technology policy engagement among decision-makers, appeared to lead to sub-par high-stakes solutions, e.g., the FR service used by Delhi Police was reported to have very low accuracy and failed to distinguish between boy and girl children [5]. 10”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [5]
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“P15 (journalist) pointed out that journalists may face disapproval for questioning certain narratives.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our study discusses how caste, religion, and tribe are eluded even within the Indian technology discourse and policy.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.1 Participatory Fair-ML knowledge systems.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that data was not always reliable due to socioeconomic factors, ML products for Indian users sufffer from double standards, and AI was seen with unquestioning aspiration.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 6 CONCLUSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The views and experiences reported by the interview respondents are representative enough of Indian AI practices to support general conclusions about India.
In the paper
“Additionally, respondents pointed to significant scope creep in high-stakes AI, e.g., a few mentioned how the tender for an FR system was initially motivated by detecting criminals, later missing children, to then arresting protestors [5].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
What the sources say
- Not checked yetcites [5]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Some respondents mentioned how a few automation solutions were announced following public sentiment, but turned into surveillance e.g., how predictive policing in Delhi and FR in train stations was announced after Nirbhaya's gruesome gangrape in 2012 and women's safety incidents [28,33].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [28]
- Not checked yetcites [33]
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many respondents described how limited debate and analysis of AI in India led to a weak implementation of”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fairness in India was reported to suffer from a lack of access to contributing datasets, APIs, and documentation, with several respondents describing how challenging it was for researchers and civil society to assess the high-stakes AI systems.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We presented a qualitative study and discourse analysis of algorithmic power in India, and found that algorithmic fairness assumptions are challenged in the Indian context.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 6 CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The views and experiences reported by the interview respondents are representative enough of Indian AI practices to support general conclusions about India.
In the paper
“Indeed, notions of algorithmic fairness seem to fit within a broad arc of enlightenment and post-enlightenment thinking, including in actuarial risk assessment [147].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.3 Philosophical roots.
What the sources say
- Not checked yetcites [147] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similarly, a study of children in three different cultures found that notions of distributive justice are not universal: “children from a partially hunter-gatherer, egalitarian African culture distributed the spoils more equally than did the other two cultures, with merit playing only a limited role” [185].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.2 Fairness perceptions across cultures
What the sources say
- Not checked yetcites [185] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Summarising decades worth of research, [120] says that the more collectivist and hierarchical societies in the East differs from the more individualistic and egalitarian cultures of the West in how different forms of justicedistributive, procedural, and retributive-are conceptualised and achieved.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.2 Fairness perceptions across cultures
What the sources say
- Not checked yetcites [120] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Conventional western AI fairness is becoming a universal ethical framework for AI; consider the AI strategies from India [1], Tunisia [205], Mexico [129], and Uruguay [13] that espouse fairness and transparency, but pay less attention to what is fair in local contexts.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
What the sources say
- Not checked yetcites [1]
- Not checked yetcites [205]
- Not checked yetcites [129] source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Indeed, researchers since the late 1960s have tried to translate US antidiscrimination law into statistical metrics [90].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.1.2 Legal framing.
What the sources say
- Not checked yetcites [90]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“While the concern for justice has a long history in the West (e.g., Aristotle, Rawls) and the East (e.g., Confucius, Chanakya), they show that the majority of empirical work on social justice has been situated in the US and Western Europe, grounding the understanding of justice in the Western cultural context.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.2 Fairness perceptions across cultures
What the sources say
- Not checked yetcites [120] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without engagement with the conditions, values, politics, and histories of the non-West, AI fairness can be a tokenism, at best-pernicious, at worst-for communities.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 1 INTRODUCTION
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For instance, [40] compared the acquisition of fairness behaviour in seven different societies: Canada, India, Mexico, Peru, Senegal, Uganda, and the USA, and found that while children from all cultures developed aversion towards disadvantageous inequity (avoid receiving less than a peer), advantageous inequity aversion (avoid receiving more than a peer) was more prevalent in the West.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.2 Fairness perceptions across cultures
What the sources say
- Not checked yetcites [40] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Researchers have documented societal biases in predictive policing in New Delhi [127], caste [202] and ethnicity [15] biases in job applications, call-center job callbacks [27], castebased wage-gaps [124], caste discrimination in agricultural loans decisions [116], and even in online matrimonial ads [157].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.4 Accountability for unfairness
What the sources say
- Not checked yetcites [127]
- Not checked yetcites [202]
- Not checked yetcites [15]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We recruited respondents via a combination of reaching out directly and personal contacts, using purposeful sampling [151]-i.e., identifying and selecting experts with relevant experience-iterative until saturation.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Recruitment and moderation
What the sources say
- Not checked yetcites [151] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Accountability is fundamentally about answerability for actions [114], and central to these are three phases by which an actor is made answerable to a forum: information-sharing, deliberation and discussion, and the imposition of consequences [214].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.4 Accountability for unfairness
What the sources say
- Not checked yetcites [114] source
- Not checked yetcites [214]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Lack of an ecosystem of tools, policies, and stakeholders like journalists, researchers, and activists to interrogate high-stakes AI inhibits meaningful fairness in India.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Representational gaps in data is documented as one of the major challenges in achieving responsible AI from a global perspective [21,190].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.3 Algorithmic fairness in the non-West
What the sources say
- Not checked yetcites [21]
- Not checked yetcites [190] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Half the Indian population lacks access to the Internet-the excluded half is primarily women, rural communities, and Adivasis [96,106,153,168,189].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.1 Data considerations.
What the sources say
- Not checked yetcites [96]
- Not checked yetcites [106]
- Not checked yetcites [153]
- Not checked yetcites [168]
- Not checked yetcites [189]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Recently, the IEEE Standards Association highlighted the monopoly of Western ethical traditions in AI ethics, and inquired how incorporating Buddhist, Ubuntu, and Shinto-inspired ethical traditions might change the processes of responsible AI [93].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.3 Algorithmic fairness in the non-West
What the sources say
- Not checked yetcites [93]
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Accountability is fundamentally about answerability for actions [114], and central to these are three phases by which an actor is made answerable to a forum: information-sharing, deliberation and discussion, and the imposition of consequences [214].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.4 Accountability for unfairness
What the sources say
- Not checked yetcites [214]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We took great care to create a research ethics protocol to protect respondent privacy and safety, especially due to the sensitive nature of our inquiry.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Recruitment and moderation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“conventional gender roles in Indian society lead to men having better access to devices, documentation, and mobility (see [64,178]), women often borrowed phones.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Gender Identity & Sexual Orientation (No official
What the sources say
- Not checked yetcites [64] source
- Not checked yetcites [178]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a societal context where the distance between models and dis-empowered communities is large-via technical distance, social distance, ethical distance, temporal distance, and physical distance-a myopic focus on localising fair model outputs alone can backfire.”
§ Re-imagining Algorithmic Fairness in India and Beyond > Unquestioning AI aspiration:
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“Nissenbaum distinguishes four barriers to responsibility in computer systems: (1) the problem of many hands, (2) bugs, (3) blaming the computer, and (4) ownership without liability [146].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 2.4 Accountability for unfairness
What the sources say
- Not checked yetcites [146]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Transcripts were coded and analyzed for patterns using an inductive approach [201].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Recruitment and moderation
What the sources say
- Not checked yetcites [201]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Many, though not all, graduates of Indian Institutes of Technology, founders of pioneering Indian software companies and nearly all Nobel prize winners of Indian origin have come from privileged castes and class backgrounds [71,196].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [71]
- Not checked yetcites [196] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Researchers have pointed to the aspirational role played by technology in India, signifying symbolic meanings of modernity and progress via technocracy [149,150,176].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [149] source
- Not checked yetcites [150]
- Not checked yetcites [176]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Reservations assigns quotas for marginalised communities at the national and state levels.7”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [164] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“reported to be attractive for improving model robustness and training data [14].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [14]
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“many have pointed to how merit is a function of caste privilege [196,207].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [196] source
- Not checked yetcites [207]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI for societal benefit is a pivotal development thrust in India, with a focus on healthcare, agriculture, education, smart cities, and mobility [1]-influencing citizen imaginaries of AI.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
What the sources say
- Not checked yetcites [1]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Depending on the policy, reservations can allocate quotas from 30% to 80%.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Equipped with microphones, GPS, cameras, and a SIM, the trackers allowed detailed surveillance of movement and work, leading to some women workers avoiding restrooms for fear of camera capture, avoiding sensitive conversations for fear of snooping, and waiting for the tracker to die before going home [111].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
What the sources say
- Not checked yetcites [111]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Indian film stars are overwhelmingly fair-skinned, conventionally attractive, and able-bodied [108], not fully representative of the larger society.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [108] source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Originally designed for Dalits and Adivasis, reservations have expanded to include other backward castes, women, persons with disabilities, and religious minorities.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [164] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While engineers and researchers are mostly privileged everywhere, the stark socio-economic disparities between Indian engineers and the marginalised communities may further amplify the distances.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.2 Double standards by ML makers
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“One of the restorative justice measures to repair centuries of subordination-reservations are a type of affirmative action enshrined in the Indian constitution [164].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
What the sources say
- Not checked yetcites [164] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Contrast with the West, where a large, active stakeholder ecosystem (of civil society, journalists, and law makers) is AI-literate and has access to open APIs and data.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.3 Unquestioning AI aspiration
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Reservations in India have been described as one of the world's most radical policies [25] (see [164] for more).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 4.1.2 Model considerations.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI is even more aspirational and consequential in non-Western nations, where it performs an 'efficiency' function in distributing scarce socio-economic resources and differentiating economies.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.3 Enabling Fair-ML Ecosystems
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“AI is increasingly perceived as a masculine, hype-filled, technoutopian enterprise, with nations turning into AI superpowers [170].”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.3 Enabling Fair-ML Ecosystems
What the sources say
- Not checked yetcites [170]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The considerations we identified are certainly not limited to India; likewise, we call for inclusively evolving global approaches to Fair-ML.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 6 CONCLUSION
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Low costs, large and diverse populations, and policy infirmities have been cited as reasons for following double standards in India, e.g., in non-consensual human trials and waste dumping [123] (also see [137]).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.3 First-world care in deployments.
What the sources say
- Not checked yetcites [123]
- Not checked yetcites [137]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For instance, personal names act as a signifier for various socio-demographic attributes in India, however there are no large datasets of Indian names (like the US Census data, or the SSA data) that are readily available for fairness evaluations.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.1.2 Model and model (un)fairness considerations.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“e.g., according to the National Crime Records Bureau (NCRB) in 2015, 34% of undertrials were Dalits and Adivasis (25% of the population); 20% were Muslims (14% of population); and 70% were non-literate (26% of the population) [203].”
§ Re-imagining Algorithmic Fairness in India and Beyond > Algorithmic opacity and authority
What the sources say
- Not checked yetcites [203]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Recontextualising data and models can only go so far without participatorily empowering communities in identifying problems, specifying fairness expectations, and designing systems.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2 Empowering Communities
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“The quota system gives rise to the problem of matching under distributional constraints, a known problem in economics [23,79,105], but has not received attention within the FAccT community (although [51] is related).”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.1.2 Model and model (un)fairness considerations.
What the sources say
- Not checked yetcites [23] source
- Not checked yetcites [79]
- Not checked yetcites [105]
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Missing Indic factors and values in data and models, compounded by double standards and disjuncture in ML, deployed in an environment of unquestioning AI aspiration, face the risk of reinforcing injustices and harms.”
§ Re-imagining Algorithmic Fairness in India and Beyond > 5 TOWARDS AI FAIRNESS IN INDIA
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
There is a lack of substantial policy or research on advancing algorithmic fairness for India's large AI-interfacing population.
“Despite the momentum on high-stakes AI systems, currently there is a lack of substantial policy or research on advancing algorithmic fairness for such a large population interfacing with AI.”
The paper relies on this without support.
India is on a unique path to AI characterised by pluralism, socio-economic development, technocratic nationbuilding, and uneven AI capital.
“We contend that India is on a unique path to AI, characterised by pluralism, socio-economic development, technocratic nationbuilding, and uneven AI capital-which requires us to confront many assumptions made in algorithmic fairness.”
The paper relies on this without support.
The views and experiences reported by the interview respondents are representative enough of Indian AI practices to support general conclusions about India.
“We presented a qualitative study and discourse analysis of algorithmic power in India, and found that algorithmic fairness assumptions are challenged in the Indian context.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, as existing literature makes clear, fairness is often contextual [57,90,91,131,136]: a seemingly”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [131]
In the citing paper
“Transparency which supports broader accountability processes on training sets, models and internal processes behind these services, as well as in the widersense of acknowledgement of known-limitations, past failures, and lessons learnt [27,131] can help move AIaaS from the opaque, unknown and potentially harmful 'black-boxes' of today, to a more understandable, accepting of their own limitations,”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [131]
In the citing paper
“As a part of the internal and external accountability processes, transparency can assist in facilitating algorithmic fairness.”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [131]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Re-imagining Algorithmic Fairness in India and Beyond > 5.2.1 Participatory Fair-ML knowledge systems.
“Initiatives like Design Beku [4] and SEWA [10] are excellent decolonial examples of participatorily co-designing with under-served communities.”
§ Re-imagining Algorithmic Fairness in India and Beyond > References
“10. Delna Abraham and Ojaswi Rao. [n.d.]. 84% Dead In Cow-Related Vi- olence Since 2010 Are Muslim; 97% Attacks After 2014 | IndiaSpend.”
Citations
Cites
A General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858 · cited 1×
“Analysis and coding Transcripts were coded and analyzed for patterns using an inductive approach [201].”
MethodFanon, F. (2000) · Princeton University Press eBooks · doi:10.1007/978-1-137-05194-3_4 · Cited by 7,516
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 1×
“The call for a global lens in AI accountability is not new [84, 155], but the ethical principles in AI are often interpreted, prioritised, contextualised, and implemented differently across the globe [98].”
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
Encountering development: the making and unmaking of the Third World
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The validity and practicality of sun-reactive skin types I through VI
Fitzpatrick, T. B. (1988) · Archives of Dermatology · doi:10.1001/archderm.1988.01670060015008 · Cited by 4,339 · cited 4×
“often used by researchers as a phenotype [46], but was originally developed to categorise UV sensitivity) [73].”
BackgroundInfluential(2018) · Cited by 3,510
Decolonising the Mind: The Politics of Language in African Literature
Kamoche, J. G., & Thiong’o, N. W. (1987) · World Literature Today · doi:10.2307/40143257 · Cited by 3,088 · cited 1×
“A decolonial perspective (borrowed from Latin American and African scholars like [66, 70, 72, 134, 210]) helps us confront inequalities from colonisation in India, providing new openings for knowledge and justice in AI fairness research.”
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Model Cards for Model Reporting
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Sorted by citations
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
“, 2020), or biases that are salient in non-Western socio-cultural contexts, such as caste (Sambasivan et al., 2021).”
BackgroundSelf-citation“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781
“, [31, 98, 107], understanding socio-cultural factors e.”
BackgroundSelf-citationTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“The axes along which unfair bias is encoded in the LM can be rooted in localised social hierarchies such as the Hindu caste system, making it harder to anticipate harmful social stereotypes across contexts [163].”
BackgroundEthical and social risks of harm from Language Models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022) · Big Data & Society · doi:10.1177/20539517221115189 · Cited by 316
“In one of the few studies conducted in a non-Western context, Sambasivan et al. (2021) further found that missing data and misrepresentation of subgroups in the existing data were critical reasons for algorithmic unfairness perceived.”
Background
Metadata
- Authors
- Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2021-03
- Pages
- 315-328
- OpenAlex
- W3123871787
- Semantic Scholar
- 187608bf94b2dccd25d1266ed925abf7b55dbb2e
- Cited by
- 228
- References
- 141
- Citations by year
- 2026: 60 · 2025: 45 · 2024: 40 · 2023: 40 · 2022: 30 · 2021: 13
- FWCI
- 27.85
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by-nc-sa/4.0/ (vor)
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- algorithmic fairness, decolonial AI, model fairness, data reliability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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