Position: AI Leaderboards Are Underserving the Global South: A Case Study from India
AI leaderboards lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution, making them structurally unsuited for the Global South High-quality regional benchmarks already exist (IndicSUPERB, MILU, LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic), but are excluded from global leaderboards due to institutional design failures, not data gaps Commercial pressure from Global North customers corrects leaderboard failures, while the Global South lacks e
Analysis
TL;DR
- AI leaderboards lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution, making them structurally unsuited for the Global South
- High-quality regional benchmarks already exist (IndicSUPERB, MILU, LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic), but are excluded from global leaderboards due to institutional design failures, not data gaps
- Commercial pressure from Global North customers corrects leaderboard failures, while the Global South lacks equivalent leverage to demand inclusion
- A consultation with 58 AI practitioners in India showed consistent preference for formal governance and disclosure-based conflict management
- The proposed solution is regional leaderboards with independent governance established from the start, rather than producing more benchmark data
Why It Matters
This paper exposes a critical equity gap in AI evaluation infrastructure that directly affects model deployment for over half the world's population. For AI practitioners and researchers, it highlights that benchmark inclusion is a governance problem, not a technical one—meaning solutions require institutional reform, not just more datasets. The findings are especially relevant as AI adoption accelerates in multilingual, multiculture regions that remain underserved by current evaluation frameworks.
Technical Details
- The paper identifies specific existing regional benchmarks: IndicSUPERB, MILU, and LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic—demonstrating that data quality is not the bottleneck
- India is used as the primary case study, representing 1.4 billion people and 22 scheduled languages, yet lacking any trusted benchmark aggregation mechanism
- Empirical findings come from a consultation with 58 AI practitioners in India, revealing strong consensus around formal governance structures and disclosure-based conflict-of-interest management
- The authors classify this as a position paper, arguing that the barrier is institutional design rather than missing data or technical capability
Industry Insight
- AI companies deploying in Global South markets should advocate for or establish regional leaderboard governance rather than relying on existing Global North-centric evaluations, which systematically exclude relevant benchmarks
- Benchmark organizers and platform operators should proactively implement independent governance and conflict-of-interest disclosure policies to avoid replicating the same structural exclusion
- Investors and policymakers in the Global South should treat benchmark infrastructure as critical public goods, funding regional leaderboard initiatives with built-in governance rather than treating evaluation as an afterthought
Disclaimer: The above content is generated by AI and is for reference only.