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Documenting Deployment with Fabric: A Repository of Real-World AI Governance
Created by
Haebom
Author
Mackenzie Jorgensen, Kendall Brogle, Katherine M. Collins, Lujain Ibrahim, Arina Shah, Petra Ivanovic, Noah Broestl, Gabriel Piles, Paul Dongha, Hatim Abdulhussein, Adrian Weller, Jillian Powers, Umang Bhatt
Outline
This paper introduces Fabric, an open repository that illuminates the governance mechanisms of deployed AI use cases. Unlike previous research that focuses on the risks of AI use, this study explores the increasing social integration of AI. The researchers collected 20 AI use cases through semi-structured interviews with practitioners and collaboratively designed AI workflow diagrams to analyze the oversight mechanisms and safeguards of AI use. The Fabric repository includes visual diagrams of AI use cases and descriptions of deployment systems, identifying governance gaps and common patterns in human oversight of deployed AI systems. Fabric is intended to serve as a scalable and evolving tool for studying the effectiveness of AI governance.
Takeaways, Limitations
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Takeaways:
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Provides an empirical understanding of the governance mechanisms of deployed AI systems.
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Provides new datasets and tools (Fabric) for AI governance research.
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We identify common oversight patterns and governance gaps in AI use cases and suggest future research directions.
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It presents a realistic picture of AI governance based on the experiences of practitioners.
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Limitations:
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Currently, only 20 AI use cases are included, which may limit generalizability.
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There is a possibility of bias in the selected AI use cases.
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Potential for subjective interpretation due to interview-based data collection method.
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Continuous updates and maintenance of the Fabric repository are required.