Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure
This addresses the problem of balancing innovation with risk assessment in AI for open-source developers and policymakers, though it is incremental in analyzing existing data.
The paper tackles the challenge of promoting responsible AI and transparency in open-source ecosystems, finding through analysis of 7903 Hugging Face projects that risk documentation is linked to evaluation practices, but high performers on competitive leaderboards show less accountability.
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.