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Re-imagining Algorithmic Fairness in India and Beyond

Nithya Sambasivan
Tulsee Doshi
FAccT 2021 (2021)


Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and optimisations. 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 in India. We find that data is not always reliable due to socio-economic factors, users are given third world treatment by ML makers, and AI signifies 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.