  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.
