No abstract

No abstract

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

No abstract

No abstract

No abstract

In India, women represent 45% of total computer science enrollment in universities, almost three times the rate in the United States, where it is 18%. At the same time, women make up an estimated 25-30% of the HCI community in India, half the rate in the U.S. We investigate the complexities of these surprising phenomena through qualitative research of Indian computer science and human-computer interaction researchers and professionals at various life stages. We find among other things that Indian familial norms play a significant role in pressuring young women into computing as a field; that familial pressures and workplace discrimination then cause a precipitous exit of women from computing at the onset of marriage; and that HCI occupies an interstitial space between art and technology that affects women's careers. Our findings underscore the societal influence on women's representation in the tech sector and invite further participation by the HCI community in related questions.