Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914959711010816 |
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| author | Chakraborti, Mahasweta Prestoza, Bert Joseph Vincent, Nicholas Frey, Seth |
| author_facet | Chakraborti, Mahasweta Prestoza, Bert Joseph Vincent, Nicholas Frey, Seth |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_19104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure Chakraborti, Mahasweta Prestoza, Bert Joseph Vincent, Nicholas Frey, Seth Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies Software Engineering 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. |
| title | Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies Software Engineering |
| url | https://arxiv.org/abs/2409.19104 |