Papers › Implicit Bias-Like Patterns in Reasoning Models
Implicit Bias-Like Patterns in Reasoning Models
Messi H. J. Lee, Calvin K. Lai
Implicit bias refers to automatic mental processes that shape perceptions, judgments, and behaviors. Previous research on "implicit bias" in LLMs focused primarily on outputs rather than the processes underlying the outputs. We present the Reasoning Model Implicit Association Test (RM-IAT) to study implicit bias-like processing in reasoning models, which are LLMs using step-by-step reasoning for complex tasks. Using RM-IAT, we find o3-mini and DeepSeek R1 require more tokens when processing association-incompatible information, mirroring human implicit bias patterns. Conversely, Claude 3.7 Sonnet displays reversed patterns for race and gender tests, requiring more tokens for association-compatible information. This reversal appears linked to differences in safety mechanism activation, increasing deliberation in sensitive contexts. These findings suggest AI systems can exhibit processing patterns analogous to both human implicit bias and bias correction mechanisms.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections