Full list
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Economic Evaluations of Language Models
Alexander Wan et al.
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Frontier AI Unevenly Exposes National Economies
Arul Murugan et al.
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Algorithmic Monocultures in Hiring
Rishi Bommasani*, Sarah H. Bana*, Kathleen A. Creel* et al.
FAccT 2026
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Unlocking AI Usage Data for Labor Market Research
Matthew Sharp et al. (2026)
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Estimating Time Spent on Work Tasks
Stephane Hatgis-Kessell et al. (2026)
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Open-world evaluations for measuring frontier AI capabilities
Sayash Kapoor et al.
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AISPA: System Prompt Auditing
Xiangning Lin et al. (2026)
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2026 International AI Safety Report
Yoshua Bengio et al.
India AI Impact Summit 2026
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The Limits of AI Data Transparency Policy: Three Disclosure Fallacies
Judy Shen et al. (2026)
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Frontier AI Auditing
Miles Brundage et al. (2026)
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Mapping the AI Supply Chain
Sarah Cen et al. (2026)
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FLARE-AI: Flaw Reporting for AI
Shayne Longpre et al.
ICML 2026
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Legal Alignment for Safe and Ethical AI
Noam Kolt et al.
TMLR 2026
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Open Technical Problems in Open-Weight AI Model Risk Management
Stephen Casper et al.
TMLR 2026 (Outstanding Survey Paper)
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The 2025 Foundation Model Transparency Index
Alexander Wan et al.
TMLR 2025
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Holistic Agent Leaderboard: The Missing Infrastructure for Agent Evaluation
Sayash Kapoor et al.
ICLR 2025
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Minimum Operating Conditions for Independent Third Party AI Evaluations
Conrad Stosz et al. (2025)
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Advancing Science- and Evidence-based AI Policy
Rishi Bommasani et al.
Science 2025
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NeurIPS should lead scientific consensus on AI policy
Rishi Bommasani
NeurIPS 2025
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Do Companies Make Good on their Voluntary Commitments to the White House?
Jennifer Wang, Kayla Huang, Kevin Klyman, Rishi Bommasani
AIES 2025
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Disclosure and Evaluation as Fairness Interventions for General-Purpose AI
Vyoma Raman et al.
AIES 2025
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Reliable and Responsible Foundation Models
Xinyu Yang et al.
TMLR 2025 (Outstanding Survey Paper)
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International AI Safety Report
Yoshua Bengio et al.
France AI Action Summit 2025
- In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
Shayne Longpre et al.
ICML 2025 (Spotlight, top 2.5% of papers)
- Language model developers should report train-test overlap
Andy Zhang et al.
ICML 2025 (Spotlight, top 2.5% of papers)
- Toward an Evaluation Science for Generative AI Systems
Laura Weidinger*, Inioluwa Deborah Raji*, et al.
National Academy of Engineering 2025
- Beyond Release: Access Considerations for Generative AI Systems
Irene Solaiman, Rishi Bommasani et al. (2025)
- The Reality of AI and Biorisk
Aidan Peppin et al.
FAccT 2025
- Considerations for Governing Open Foundation Models
Rishi Bommasani et al.
Science 2024
- Effective Mitigations for Systemic Risks from General-Purpose AI
Risto Uuk et al. (2024)
- The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Shayne Longpre*, Stella Biderman* et al.
TMLR 2024 (Outstanding Survey Paper)
- Interim International Scientific Report on the Safety of Advanced AI
Yoshua Bengio et al.
2024 AI Seoul Summit
- The 2024 Foundation Model Transparency Index
Rishi Bommasani*, Kevin Klyman* et al.
TMLR 2024
- On the Societal Impact of Open Foundation Models
Sayash Kapoor*, Rishi Bommasani* et al.
ICML 2024 (Oral, top 1.5% of papers)
- A Safe Harbor for AI Evaluation and Red Teaming
Shayne Longpre et al.
ICML 2024 (Oral, top 1.5% of papers)
- Foundation Model Transparency Reports
Rishi Bommasani et al.
AIES 2024 (Oral, top 1.5% of papers)
- Ecosystem Graphs: The Social Footprint of Foundation Models
Rishi Bommasani et al.
AIES 2024
- Trustworthy Social Bias Measurement
Rishi Bommasani, Percy Liang
AIES 2024
- AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing
Neel Guha*, Christie M. Lawrence* et al.
George Washington Law Review 2024
- The 2023 Foundation Model Transparency Index
Rishi Bommasani*, Kevin Klyman* et al.
TMLR 2024 (Outstanding Paper)
- Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
Connor Toups*, Rishi Bommasani* et al.
NeurIPS 2023
- Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs
Deepak Narayanan et al.
NeurIPS 2023
- Evaluation for Change
Rishi Bommasani
ACL 2023
- Evaluating Human-Language Model Interaction
Mina Lee et al.
TMLR 2023
- Holistic Evaluation of Language Models
Percy Liang*, Rishi Bommasani*, Tony Lee* et al.
TMLR 2023 (Best Paper)
- Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?
Rishi Bommasani et al.
NeurIPS 2022
- Emergent Abilities of Large Language Models
Jason Wei et al.
TMLR 2022 (Outstanding Survey Paper)
- Data Governance in the Age of Large-Scale Data-Driven Language Technology
Yacine Jernite et al.
FAccT 2022
- The Time Is Now to Develop Community Norms for the Release of Foundation Models
Percy Liang, Rishi Bommasani, Kathleen A. Creel, Rob Reich
- On the Opportunities and Risks of Foundation Models
Rishi Bommasani et al. (2021)
- Generalized Optimal Linear Orders
Rishi Bommasani (2020)
M.S. Thesis, Cornell University
- Intrinsic Evaluation of Summarization Datasets
Rishi Bommasani, Claire Cardie
EMNLP 2020
- Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Rishi Bommasani, Kelly Davis, Claire Cardie
ACL 2020