  Large language models (LLMs) can pass explicit social 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 challenges by introducing two new measures of bias: LLM
Implicit Bias, a prompt-based method for revealing implicit bias; and LLM
Decision Bias, a strategy to detect subtle discrimination in decision-making
tasks. Both measures are based on psychological research: LLM Implicit Bias
adapts the Implicit Association Test, widely used to study the automatic
associations between concepts held in human minds; and LLM Decision Bias
operationalizes psychological results indicating that relative evaluations
between two candidates, not absolute evaluations assessing each independently,
are more diagnostic of implicit biases. Using these measures, we found
pervasive stereotype biases mirroring those in society in 8 value-aligned
models across 4 social categories (race, gender, religion, health) in 21
stereotypes (such as race and criminality, race and weapons, gender and
science, age and negativity). Our prompt-based LLM Implicit Bias measure
correlates with existing language model embedding-based bias methods, but
better predicts downstream behaviors measured by LLM Decision Bias. These new
prompt-based measures draw from psychology's long history of research into
measuring stereotype biases based on purely observable behavior; they expose
nuanced biases in proprietary value-aligned LLMs that appear unbiased according
to standard benchmarks.
