1 citations · 1 across the 5 of their papers we have counts for
5 papers
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Avijit Ghosh, Anka Reuel, Jenny Chim +45
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers can…
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Sushant Gautam, Finn Schwall, Annika Willoch Olstad +6
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this sett…
Restricting the Model, Missing the System: Measurement and Accountability in Offensive AI Governance
Michael A. Riegler, Michael Alexander Riegler, Inga Strümke +5
We show that the instruments used to measure AI offensive capability fail in two ways: (i) they overstate harm, and (ii) they credit the model with capability that belongs to the s…
The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
Sushant Gautam, Annika W. Olstad, Klas H. Pettersen +1
Moltbook is a social media platform in which posts and comments are authored exclusively by autonomous AI agents. We present the Moltbook Observatory Archive, an incremental datase…
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…