Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks

TL;DR

This paper examines how personality traits in Large Language Models influence the manifestation of cognitive biases in decision-making tasks and evaluates strategies to mitigate these biases.

Contribution

It introduces a novel analysis of personality-driven bias dynamics in LLMs and assesses the effectiveness of mitigation techniques across different model architectures.

Findings

Six prevalent cognitive biases identified.

Personality traits significantly influence bias levels.

Certain traits enhance bias mitigation effectiveness.

Abstract

Large Language Models (LLMs) are increasingly used in decision-making, yet their susceptibility to cognitive biases remains a pressing challenge. This study explores how personality traits influence these biases and evaluates the effectiveness of mitigation strategies across various model architectures. Our findings identify six prevalent cognitive biases, while the sunk cost and group attribution biases exhibit minimal impact. Personality traits play a crucial role in either amplifying or reducing biases, significantly affecting how LLMs respond to debiasing techniques. Notably, Conscientiousness and Agreeableness may generally enhance the efficacy of bias mitigation strategies, suggesting that LLMs exhibiting these traits are more receptive to corrective measures. These findings address the importance of personality-driven bias dynamics and highlight the need for targeted mitigation approaches to improve fairness and reliability in AI-assisted decision-making.