English

Talent or Luck? Evaluating Attribution Bias in Large Language Models

Computation and Language 2026-04-30 v2

Abstract

When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event outcomes, shapes perceptions, reinforces stereotypes, and influences decisions. Attribution Theory in social psychology explains how humans assign responsibility for events using implicit cognition, attributing causes to internal (e.g., effort, ability) or external (e.g., task difficulty, luck) factors. LLMs' attribution of event outcomes based on demographics carries important fairness implications. Most works exploring social biases in LLMs focus on surface-level associations or isolated stereotypes. This work proposes a cognitively grounded bias evaluation framework to identify how models' reasoning disparities channelize biases toward demographic groups.

Keywords

Cite

@article{arxiv.2505.22910,
  title  = {Talent or Luck? Evaluating Attribution Bias in Large Language Models},
  author = {Chahat Raj and Mahika Banerjee and Jinhao Pan and Aylin Caliskan and Antonios Anastasopoulos and Ziwei Zhu},
  journal= {arXiv preprint arXiv:2505.22910},
  year   = {2026}
}

Comments

Accepted to ACL Findings 2026