4 citations · 8 across the 9 of their papers we have counts for
14 papers
The Limits of AI Data Transparency Policy: Three Disclosure Fallacies
Judy Hanwen Shen, Ken Liu, Angelina Wang +7
Data transparency has emerged as a rallying cry for addressing concerns about AI: data quality, privacy, and copyright chief among them. Yet while these calls are crucial for accou…
CoPE: A Small Language Model for Steerable and Scalable Content Labeling
Samidh Chakrabarti, David Willner, Kevin Klyman +3
This paper details the methodology behind CoPE, a policy-steerable small language model capable of fast and accurate content labeling. We present a novel training curricula called…
The 2025 Foundation Model Transparency Index
Alexander Wan, Kevin Klyman, Sayash Kapoor +5
Foundation model developers are among the world's most important companies. As these companies become increasingly consequential, how do their transparency practices evolve? The 20…
From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders
Amy Winecoff, Kevin Klyman
Generative AI systems may pose serious risks to individuals vulnerable to eating disorders. Existing safeguards tend to overlook subtle but clinically significant cues, leaving man…
SpecEval: Evaluating Model Adherence to Behavior Specifications
Ahmed Ahmed, Kevin Klyman, Yi Zeng +2
Companies that develop foundation models publish behavioral guidelines they pledge their models will follow, but it remains unclear if models actually do so. While providers such a…
Do AI Companies Make Good on Voluntary Commitments to the White House?
Jennifer Wang, Kayla Huang, Kevin Klyman +1
Voluntary commitments are central to international AI governance, as demonstrated by recent voluntary guidelines from the White House to the G7, from Bletchley Park to Seoul. How d…