research-article

Open access

Re-imagining Algorithmic Fairness in India and Beyond

Authors:

Nithya Sambasivan,

Ben Hutchinson,

Vinodkumar PrabhakaranAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Pages 315 - 328

https://doi.org/10.1145/3442188.3445896

Published: 01 March 2021 Publication History

Abstract

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.

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Rifat MSafir ASaha SJunaed JSaleki MAmin MAhmed S(2024)Data, Annotation, and Meaning-Making: The Politics of Categorization in Annotating a Dataset of Faith-based Communal ViolenceProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659030(2148-2156)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659030
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