44 citations · 117 across the 26 of their papers we have counts for
11 papers · 1 filter
Open Technical Problems in Open-Weight AI Model Risk Management
Stephen Casper, Kyle O'Brien, Shayne Longpre +19
Frontier AI models with openly available weights are steadily becoming more powerful and widely adopted. However, compared to proprietary models, open-weight models pose different…
FLARE-AI: Flaw Reporting for AI
Shayne Longpre, Elaine Zhu, Carson Ezell +15
Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify…
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…
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…
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…
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…
Acceptable Use Policies for Foundation Models
Kevin Klyman
As foundation models have accumulated hundreds of millions of users, developers have begun to take steps to prevent harmful types of uses. One salient intervention that foundation…
AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies
Yi Zeng, Yu Yang, Andy Zhou +9
Foundation models (FMs) provide societal benefits but also amplify risks. Governments, companies, and researchers have proposed regulatory frameworks, acceptable use policies, and…
AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies
Yi Zeng, Kevin Klyman, Andy Zhou +6
We present a comprehensive AI risk taxonomy derived from eight government policies from the European Union, United States, and China and 16 company policies worldwide, making a sig…
On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman +22
Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defin…