Conventional algorithmic fairness is West-centric, as seen in its subgroups, 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. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.
No abstract
Hyperlocal information systems cater to the needs of the people in a well-defined geographical area. Such information systems are ubiquitously accessible to the users in the developed world through their mobile phones. In developing countries hyperlocal information is valued, but often dispersed, constantly evolving, and poorly accessible. Additionally, most of the social information systems in developing countries are adopted from the developed world, and often fail to take into consideration local socio-geographical settings and cultural complexities. As a result, there is a strong reliance on word of mouth through known and unknown networks. In this paper, we present foundational insights on user needs, behaviors and motivations for the design of hyperlocal information systems in developing countries based on a multi-year, multi-method research study with around 5000 participants in three such countries - India, Indonesia and Nigeria. We also discuss how the complexity of real-world sociocultural phenomena and heterogeneity of neighborhoods impact hyperlocal information, which can further inform design of such systems in the future.
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