Talent or Luck? Evaluating Attribution Bias in Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915964857090048 |
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| author | Raj, Chahat Banerjee, Mahika Pan, Jinhao Caliskan, Aylin Anastasopoulos, Antonios Zhu, Ziwei |
| author_facet | Raj, Chahat Banerjee, Mahika Pan, Jinhao Caliskan, Aylin Anastasopoulos, Antonios Zhu, Ziwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Talent or Luck? Evaluating Attribution Bias in Large Language Models Raj, Chahat Banerjee, Mahika Pan, Jinhao Caliskan, Aylin Anastasopoulos, Antonios Zhu, Ziwei Computation and Language 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. |
| title | Talent or Luck? Evaluating Attribution Bias in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.22910 |