TransparencyAccountabilityTrustworthinesscs.LG
Foundation Model Transparency Reports
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang
February 26, 2024
AAAI/ACM Conference on AI, Ethics, and Society
45 citations
1 influential
Key Takeaways
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Transparency reports should be standardized early: The paper proposes creating Foundation Model Transparency Reports now, while the foundation model industry is still developing, rather than waiting for external pressure or documented harms—learning from how social media companies were forced to report on societal impacts only after problems emerged.
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A comprehensive framework already exists: The authors identified 100 specific transparency indicators that foundation model developers should report on, providing a concrete starting point for what information should be disclosed about how these powerful AI systems are built and deployed.
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Regulatory alignment can reduce burden: The paper found significant overlap between transparency requirements across six major government policies (including the EU AI Act and US Executive Order), suggesting that well-designed transparency reports could help companies comply with multiple regulations simultaneously rather than creating separate documentation for each jurisdiction.
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Learning from social media's mistakes: The report identifies six design principles for foundation model transparency based on both successes and shortcomings of social media transparency reporting, suggesting the field can avoid repeating past mistakes in how companies disclose information about their technologies' impacts.
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Industry action is needed now: The authors encourage foundation model developers to voluntarily publish regular transparency reports, aligned with recommendations from the G7 and White House, positioning transparency as a proactive responsibility rather than a reactive compliance requirement.