title: Implicit Bias-Like Patterns in Reasoning Models
summary: 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.
paper_authors._id: 67e0530f151ca9ed9265e949
paper_authors.name: Messi H. J. Lee
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-23T18:30:22.473Z
paper_authors._id: 64c5d832d68946edad7d5536
paper_authors.avatarUrl: /avatars/5d38217d4ab99cdcf53c52661b8baa0d.svg
paper_authors.fullname: Messi Lee
paper_authors.type: user
paper_authors.user: l048596
paper_authors._id: 67e0530f151ca9ed9265e94a
paper_authors.name: Calvin K. Lai
paper_title: Implicit Bias-Like Patterns in Reasoning Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Reasoning Model Implicit Association Test (RM-IAT) association-incompatible association-compatible large language models (LLMs)