Re-imagining Algorithmic Fairness in India and Beyond
This addresses algorithmic fairness for marginalized communities in India, highlighting systemic issues beyond technical solutions, though it is incremental in re-framing existing discourse.
The paper challenges West-centric algorithmic fairness assumptions by analyzing AI power in India through 36 qualitative interviews and discourse analysis, finding that data reliability issues, double standards by ML makers, and uncritical AI aspiration require re-contextualizing data/models and empowering oppressed communities rather than just localizing fairness.
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.