3 papers
cs.CL2026
SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models
Alok Abhishek, Tushar Bandopadhyay, Lisa Erickson
Large language models (LLMs) are increasingly deployed in high-stakes domains, where rare but severe failures can result in irreversible harm. However, prevailing evaluation benchm…
cs.CL2025
Data and AI governance: Promoting equity, ethics, and fairness in large language models
Alok Abhishek, Lisa Erickson, Tushar Bandopadhyay
In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and valida…
cs.CL2025
BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models
Alok Abhishek, Lisa Erickson, Tushar Bandopadhyay
In this research, we introduce BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models (LLMs). Building upon the BEATS framework, we…