1 citations · 1 across the 5 of their papers we have counts for
6 papers
Does Thinking Help Fairness? Reasoning Tokens Resolve Some Biases but Create More
Deng Pan, Joe Germino, Yihong Ma +4
Thinking in reasoning language models (RLMs) has been subject to debate on whether it resolves or amplifies bias. Prior works have shown competing conclusions in both directions. U…
Auto-BenchmarkCard: Automated Synthesis of Benchmark Documentation
Aris Hofmann, Inge Vejsbjerg, Dhaval Salwala +1
We present Auto-BenchmarkCard, a workflow for generating validated descriptions of AI benchmarks. Benchmark documentation is often incomplete or inconsistent, making it difficult t…
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly +17
The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack o…
Usage Governance Advisor: From Intent to AI Governance
Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi +9
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deploy…
Granite Guardian
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…
BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks
Anna Sokol, Elizabeth Daly, Michael Hind +4
Large language models (LLMs) are powerful tools capable of handling diverse tasks. Comparing and selecting appropriate LLMs for specific tasks requires systematic evaluation method…