Non-portability of Algorithmic Fairness in India
This paper highlights the non-portability of Western algorithmic fairness for Indian communities, posing a challenge for researchers and practitioners aiming to deploy fair AI systems in diverse geo-cultural contexts.
This paper investigates the applicability of Western algorithmic fairness concepts to India, finding significant challenges due to differences in sub-groups, values, and optimizations. Through 36 expert interviews and analysis of Indian algorithmic deployments, it identifies three clusters of challenges, arguing against simple translation and advocating for a re-contextualized approach to Fair-ML.
Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algorithmic fairness are to a different geo-cultural context such as India. Based on 36 expert interviews with Indian scholars, and an analysis of emerging algorithmic deployments in India, we identify three clusters of challenges that engulf the large distance between machine learning models and oppressed communities in India. We argue that a mere translation of technical fairness work to Indian subgroups may serve only as a window dressing, and instead, call for a collective re-imagining of Fair-ML, by re-contextualising data and models, empowering oppressed communities, and more importantly, enabling ecosystems.