44 citations · 117 across the 26 of their papers we have counts for
10 papers · 1 filter
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…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
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…
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Jennifer King, Kevin Klyman, Emily Capstick +2
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as th…
A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
Camille François, Ludovic Péran, Ayah Bdeir +17
The rapid rise of open-weight and open-source foundation models is intensifying the obligation and reshaping the opportunity to make AI systems safe. This paper reports outcomes fr…
New Tools are Needed for Tracking Adherence to AI Model Behavioral Use Clauses
Daniel McDuff, Tim Korjakow, Kevin Klyman +1
Foundation models have had a transformative impact on AI. A combination of large investments in research and development, growing sources of digital data for training, and architec…
In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
Shayne Longpre, Kevin Klyman, Ruth E. Appel +31
The widespread deployment of general-purpose AI (GPAI) systems introduces significant new risks. Yet the infrastructure, practices, and norms for reporting flaws in GPAI systems re…