Re-imagining Algorithmic Fairness in India and Beyond

Sambasivan, Nithya; Arnesen, Erin; Hutchinson, Ben; Doshi, Tulsee; Prabhakaran, Vinodkumar

doi:10.1145/3442188.3445896

Cited by 102 publications

(84 citation statements)

References 98 publications

(76 reference statements)

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“…Research done by Sambasivan et al show that typical Western takes on algorithmic fairness are not portable in the context of the cultural values and standards in India. 30 Similar conclusions may hold true for other regions within the Global South. In regions where ethnic, tribal, or religious affiliation hold more power in society compared with Western notions of race, which do not apply in countries such as Nigeria or Pakistan, these demographics should be seriously considered in the development, deployment, and use of AI systems.…”

Section: Understanding the Known Harms And Impact Of Ai-forhealth Interventionsmentioning

confidence: 55%

Optimizing human-centered AI for healthcare in the Global South

Okolo

2022

Patterns

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Over the past 60 years, artificial intelligence (AI) has made significant progress, but most of its benefits have failed to make a significant impact within the Global South. Current practices that have led to biased systems will prevent AI from being actualized unless significant efforts are made to change them. As technical advances in AI and an interest in solving new problems lead researchers and tech companies to develop AI applications that target the health of marginalized communities, it is crucially important to study how AI can be used to empower those on the front lines in the Global South and how these tools can be optimally designed for marginalized communities. This perspective examines the landscape of AI for healthcare in the Global South and the evaluations of such systems and provides tangible recommendations for AI practitioners and human-centered researchers to incorporate in the development of AI systems for use with marginalized populations.Concept: Basic principles of a new data science output observed and reported ll

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Section: Understanding the Known Harms And Impact Of Ai-forhealth Interventionsmentioning

confidence: 55%

Optimizing human-centered AI for healthcare in the Global South

Okolo

2022

Patterns

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“…We hope these experimental results will encour- (Sambasivan et al, 2021). These factors lead to the use of a variety of metrics to evaluate biases (Section 3).…”

Section: On How Different Decoding Properties Affect Biases In Generationmentioning

confidence: 99%

Societal Biases in Language Generation: Progress and Challenges

Sheng

Chang

Natarajan

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.

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“…28,81], so as to resist the "portability trap" of believing that one can simply port AI systems developed in and for one context to others [70]. This may involve developing AI systems (and disaggregated evaluations) in ways that are responsive to local values and norms, as in recent research to re-imagine the fairness of AI systems in India [67], research that has highlighted the risks of algorithmic colonization [11] and efforts toward decolonial AI [49], or, more generally, approaches for "designing for the pluriverse" [23]. However, approaches to the development of AI systems that are responsive local conditions will likely require slow and careful work that is fundamentally at odds with the "pedal to the metal" (P7, T4) approach incentivized by business imperatives.…”

Section: Implications Of Deploying Ai Systems At Scalementioning

confidence: 99%

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Madaio¹,

Egede²,

Subramonyam³

et al. 2021

Preprint

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Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior research has highlighted gaps between the intended design of these tools and practices and their use within particular contexts, including gaps caused by the role that organizational factors play in shaping fairness work. In this paper, we investigate these gaps for one such practice: disaggregated evaluations of AI systems, intended to uncover performance disparities between demographic groups. By conducting semi-structured interviews and structured workshops with thirty-three AI practitioners from ten teams at three technology companies, we identify practitioners' processes, challenges, and needs for support when designing disaggregated evaluations. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups on which to focus, and collecting datasets with which to conduct disaggregated evaluations. More generally, we identify impacts on fairness work stemming from a lack of engagement with direct stakeholders, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.

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Re-imagining Algorithmic Fairness in India and Beyond

Cited by 102 publications

References 98 publications

Optimizing human-centered AI for healthcare in the Global South

Optimizing human-centered AI for healthcare in the Global South

Societal Biases in Language Generation: Progress and Challenges

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

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