Implicit Bias-Like Patterns in Reasoning Models
Implicit bias refers to automatic or spontaneous mental processes that shape
perceptions, judgments, and behaviors. Previous research examining `implicit
bias' in large language models (LLMs) has often approached the phenomenon
differently than how it is studied in humans by focusing primarily on model
outputs rather than on model processing. To examine model processing, we
present a method called the Reasoning Model Implicit Association Test (RM-IAT)
for studying implicit bias-like patterns in reasoning models: LLMs that employ
step-by-step reasoning to solve complex tasks. Using this method, we find that
reasoning models require more tokens when processing association-incompatible
information compared to association-compatible information. These findings
suggest AI systems harbor patterns in processing information that are analogous
to human implicit bias. We consider the implications of these implicit
bias-like patterns for their deployment in real-world applications.
67e0530f151ca9ed9265e949
Messi H. J. Lee
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2025-03-23T18:30:22.473Z
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Messi H.J. Lee
user
leemessi
67e0530f151ca9ed9265e94a
Calvin K. Lai
Implicit Bias-Like Patterns in Reasoning Models
Implicit bias refers to automatic or spontaneous mental processes that shape
perceptions, judgments, and behaviors. Previous research examining `implicit
bias' in large language models (LLMs) has often approached the phenomenon
differently than how it is studied in humans by focusing primarily on model
outputs rather than on model processing. To examine model processing, we
present a method called the Reasoning Model Implicit Association Test (RM-IAT)
for studying implicit bias-like patterns in reasoning models: LLMs that employ
step-by-step reasoning to solve complex tasks. Using this method, we find that
reasoning models require more tokens when processing association-incompatible
information compared to association-compatible information. These findings
suggest AI systems harbor patterns in processing information that are analogous
to human implicit bias. We consider the implications of these implicit
bias-like patterns for their deployment in real-world applications.
Research introduces RM-IAT to study implicit bias-like patterns in reasoning models, revealing LLMs require more tokens for association-incompatible information, akin to human implicit bias.
Reasoning Model Implicit Association Test (RM-IAT) association-incompatible association-compatible large language models (LLMs)