Measuring Implicit Bias in Explicitly Unbiased Large Language Models
Xuechunzi Bai Angelina Wang Ilia Sucholutsky Thomas L. Griffiths
Large language models (LLMs) can pass explicit bias tests but still harbor
implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit
subtle biases. Measuring such implicit biases can be a challenge: as LLMs
become increasingly proprietary, it may not be possible to access their
embeddings and apply existing bias measures; furthermore, implicit biases are
primarily a concern if they affect the actual decisions that these systems
make. We address both of these challenges by introducing two measures of bias
inspired by psychology: LLM Implicit Association Test (IAT) Bias, which is a
prompt-based method for revealing implicit bias; and LLM Decision Bias for
detecting subtle discrimination in decision-making tasks. Using these measures,
we found pervasive human-like stereotype biases in 6 LLMs across 4 social
domains (race, gender, religion, health) and 21 categories (weapons, guilt,
science, career among others). Our prompt-based measure of implicit bias
correlates with embedding-based methods but better predicts downstream
behaviors measured by LLM Decision Bias. This measure is based on asking the
LLM to decide between individuals, motivated by psychological results
indicating that relative not absolute evaluations are more related to implicit
biases. Using prompt-based measures informed by psychology allows us to
effectively expose nuanced biases and subtle discrimination in proprietary LLMs
that do not show explicit bias on standard benchmarks.