Re-imagining Algorithmic Fairness in India and Beyond (2101.09995v2)

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.

• The paper redefines algorithmic fairness by critiquing Western-centric models and highlighting India’s unique socio-economic and cultural challenges.
• It employs 36 qualitative interviews to reveal issues like unreliable data, double standards in AI deployment, and the neglect of factors like caste and class.
• It proposes a holistic, participatory framework that adapts fairness metrics and empowers marginalized communities for equitable AI systems.

Re-imagining Algorithmic Fairness in India and Beyond

In the paper "Re-imagining Algorithmic Fairness in India and Beyond," the authors challenge the Western-centric paradigm of algorithmic fairness by providing insights from an extensive qualitative paper conducted in India. The paper’s findings reveal that traditional notions of algorithmic fairness, rooted in Western contexts, often fail to account for the complexities and socio-economic realities of Indian society.

Dissecting the Western Fairness Framework

The paper critiques the dominant fairness frameworks that have emerged primarily from Western contexts, focusing on race and gender as primary axes of discrimination. Among its critical observations, the authors emphasize that these frameworks often neglect other crucial dimensions of marginalization that are prevalent in India, such as caste, class, and religion. In this context, the reliance on datasets and models originating from Western-centric assumptions can lead to significant biases when applied to Indian contexts.

Key Findings and Discussions

Through 36 qualitative interviews and an analysis of discourse on AI deployments in India, the authors arrive at several noteworthy findings:

• Unreliable Data: Data reliability in India is compromised by factors such as socio-economic disparities, digital divide, and infrastructural limitations. The authors note that half of the Indian population lacks internet access, skewing datasets towards more privileged groups, often middle-class men, and further excluding marginalized communities from AI relevance.
• Double Standards in AI Deployment: There is a perceptible difference in AI application standards between Western and Indian contexts. AI technologies deployed in India often subject communities to intrusive data practices, with limited recourse available in cases of biases or errors.
• Cultural Specificity and Fairness: Western fairness paradigms do not fully apply to India, where the social fabric is significantly different. Caste, for instance, is a distinct axis of inequity that demands unique fairness strategies.

Proposed Framework for Algorithmic Fairness in India

The paper proposes a holistic framework to operationalize algorithmic fairness in India. It centers on three primary pathways:

1. Recontextualizing Data and Models: This involves adapting existing algorithmic fairness evaluations to better suit the Indian context by addressing data representations, defining culturally relevant fairness metrics, and embracing local epistemologies.
2. Empowering Communities: Recognizing the agency of marginalized groups and incorporating their insights into the AI development cycle can lead to more equitable outcomes. Bridging the digital divide with accessible technologies is a priority.
3. Enabling Fair-ML Ecosystems: Building ecosystems involving various stakeholders such as civil society, researchers, and policymakers can foster accountability and transparency in AI deployments.

Implications and Future Directions

The implications of this research are manifold and underscore the necessity for a context-aware approach to AI fairness. Practically, this means acknowledging non-Western paradigms of justice and representation within AI systems. Theoretically, it raises critical questions about the universality of ethical AI frameworks. As AI continues to expand globally, diverse geopolitical landscapes necessitate innovative and flexible algorithmic fairness frameworks.

Crucially, the paper advocates for a participatory design approach to AI system development—one that appropriately involves marginalized communities in the AI lifecycle. This approach could serve as a model for other non-Western contexts, where similar socio-economic dynamics could influence AI adoption and fairness.

In conclusion, the research represents a pivotal contribution to the discourse on global AI ethics, paving the way for more inclusive and equitable AI systems that account for the diverse socio-cultural fabric of non-Western societies.