Re-imagining Algorithmic Fairness in India and Beyond

TL;DR

This paper critiques the Western-centric approach to algorithmic fairness, analyzing AI deployment in India through qualitative research, and proposes a localized, community-empowering framework for fair machine learning.

Contribution

It challenges assumptions of Western-centric fairness, offers a contextual analysis of AI in India, and proposes a new roadmap for fair ML ecosystems tailored to local socio-economic realities.

Findings

Data reliability issues due to socio-economic factors in India

ML practitioners often follow double standards

AI fosters unquestioning aspiration among communities

Abstract

Conventional algorithmic fairness is West-centric, as seen in its sub-groups, 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.