BiasGym: Fantastic Biases and How to Find (and Remove) Them
Authors: name: Sekh Mainul Islam; name: Nadav Borenstein; name: Siddhesh Milind Pawar; name: Haeun Yu; name: Arnav Arora; name: Isabelle Augenstein
Published: 2025-08-12 11:23:44

Abstract: Understanding biases and stereotypes encoded in the weights of Large Language
Models (LLMs) is crucial for developing effective mitigation strategies. Biased
behaviour is often subtle and non-trivial to isolate, even when deliberately
elicited, making systematic analysis and debiasing particularly challenging. To
address this, we introduce BiasGym, a simple, cost-effective, and generalizable
framework for reliably injecting, analyzing, and mitigating conceptual
associations within LLMs. BiasGym consists of two components: BiasInject, which
injects specific biases into the model via token-based fine-tuning while
keeping the model frozen, and BiasScope, which leverages these injected signals
to identify and steer the components responsible for biased behavior. Our
method enables consistent bias elicitation for mechanistic analysis, supports
targeted debiasing without degrading performance on downstream tasks, and
generalizes to biases unseen during training. We demonstrate the effectiveness
of BiasGym in reducing real-world stereotypes (e.g., people from a country
being `reckless drivers') and in probing fictional associations (e.g., people
from a country having `blue skin'), showing its utility for both safety
interventions and interpretability research.