arXiv cs.AI论文
AI排行榜未能服务全球南方：来自印度的案例研究
本文认为AI排行榜在制度设计上不适合服务全球南方，因为缺乏独立治理、利益冲突政策和指标演进机制。问题不在于缺少数据，而在于制度设计。全球排行榜未纳入已有的高质量区域基准，且商业压力无法纠正对全球南方用户的影响。基于对58位AI从业者的咨询，作者建议建立具有独立治理的区域排行榜。
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Abstract:This position paper argues that AI leaderboards are structurally ill-suited to serving the Global South because they lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution. The barrier is not missing data; high-quality regional benchmarks already exist: IndicSUPERB, MILU, and LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic. The barrier is institutional design. Global leaderboards do not include these benchmarks, and no governance mechanism compels them to do so. Commercial pressure corrects leaderboard failures when paying customers in the Global North are affected. The Global South lacks equivalent leverage. Without governance, failures affecting Hindi, Swahili, or Arabic speakers persist indefinitely as documented but unaddressed gaps. Using India as a case study (1.4 billion people, 22 scheduled languages, high-quality benchmarks, but no trusted aggregation), we report findings from a consultation with 58 AI practitioners showing consistent preference for formal governance and disclosure-based conflict management. The solution is not more data but better institutions: regional leaderboards with independent governance from the start.