Responsible AI in the OSS: Reconciling Innovation with Risk Assessment and Disclosure

Ethical concerns around AI have increased emphasis on model auditing and reporting requirements. We thoroughly review the current state of governance and evaluation practices to identify specific challenges to responsible AI development in OSS. We then analyze OSS projects to understand if model evaluation is associated with safety assessments, through documentation of limitations, biases, and other risks. Our analysis of 7902 Hugging Face projects found that while risk documentation is strongly associated with evaluation practices, high performers from the platform’s largest competitive leaderboard (N=789) were less accountable. Recognizing these delicate tensions from performance incentives may guide providers in revisiting the objectives of evaluation and legal scholars in formulating platform interventions and policies that balance innovation and responsibility.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i1.36567
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Responsible AI in the OSS: Reconciling Innovation with Risk Assessment and Disclosure

Seth Frey, Mahasweta Chakraborti, Vladimir Filkov, Bert Joseph Prestoza et al.
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Ethics and Social Impacts of AI
article

Responsible AI in the OSS: Reconciling Innovation with Risk Assessment and Disclosure

Seth Frey, Mahasweta Chakraborti, Vladimir Filkov, Bert Joseph Prestoza, Nicholas Vincent
article en

Abstract

Ethical concerns around AI have increased emphasis on model auditing and reporting requirements. We thoroughly review the current state of governance and evaluation practices to identify specific challenges to responsible AI development in OSS. We then analyze OSS projects to understand if model evaluation is associated with safety assessments, through documentation of limitations, biases, and other risks. Our analysis of 7902 Hugging Face projects found that while risk documentation is strongly associated with evaluation practices, high performers from the platform’s largest competitive leaderboard (N=789) were less accountable. Recognizing these delicate tensions from performance incentives may guide providers in revisiting the objectives of evaluation and legal scholars in formulating platform interventions and policies that balance innovation and responsibility.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(1)
Simon Fraser University (CA), University of California, Davis (US)
National Science Foundation
Openalex Percentile: Top 71%
Ethics and Social Impacts of AI
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