开源AI模型评估影响风险披露，高分模型反而更少公开风险。
Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure
- 分析7903个Hugging Face项目，发现评估实践与风险文档强相关。
- 主流排行榜中879个高分模型的披露率显著低于平均水平。
- 为政策设计提供依据，平衡开源创新与伦理责任。
AI的快速扩展促使对开发与实践中的伦理问题日益关注，催生了日益复杂的模型审计与报告要求及治理框架以降低对个人与社会的潜在风险。在这一关键节点，本文审视了非正式领域（如支持关键基础设施且广泛应用的开源软件）推动负责任AI与透明度所面临的实际挑战。重点关注模型性能评估如何促进或阻碍对模型局限性、偏见及其他风险的探查。对Hugging Face平台7903个项目的受控分析显示，风险文档与评估实践呈强关联。然而，平台上最热门竞赛榜单的789个提交结果表明，高性能模型在问责方面表现更弱。研究结果可为AI提供方与法律学者设计干预措施与政策提供参考，以在保护开源创新的同时激励伦理采纳。
原文摘要 · Abstract (English)
The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.
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