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Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models
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Conflicts of Interest
Identified Weaknesses
Rating Explanation
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Paper Summary
Paperzilla title
LLMs Show Bias in Salary Advice, Less So in Knowledge Tests
This study investigates bias in several large language models (LLMs) across knowledge assessments and simulated salary negotiations. While limited persona variety, noisy evaluation, and narrow scenarios limit generalizability, the study finds LLMs exhibit more pronounced bias in socio-economic contexts, like suggesting lower salaries for certain demographics, than in knowledge tests.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Limited Persona Scope
The study acknowledges testing only a limited set of personas, focusing on sex, ethnicity, and migrant status, but excluding other factors like gender identity, sexual orientation, religion, age, etc. This limited scope may not fully capture the complexities of bias in LLMs and could miss other forms of discrimination.
Limited Benchmark and Language
The evaluation is solely based on the MMLU benchmark and a salary negotiation scenario, both in English. Relying on a single benchmark and language may not be representative of LLMs' performance and bias across diverse tasks and languages.
Noisy Evaluation Method
Although statistical tests were used, the generative evaluation method employed in Experiments 1 and 2 is known to be noisy and sensitive to minor prompt changes. This could affect the reliability of the results, especially given that experiments were run only once for each combination.
Limited Generalizability of Salary Negotiation
The salary negotiation scenario in Experiment 3 is limited to a single US city (Denver) and a specific job title ('Specialist'). The results might not generalize to other locations, job titles, or cultural contexts, limiting their external validity.
Limited Socio-Economic Factors
The study focuses on a single socio-economic factor (pay gap) and doesn't explore other relevant factors like wealth, education, or social status, which could also influence LLM bias.
Limited number of LLMs
The study is limited to only 5 commercially available large language models which might affect the generalizability of the results.
Rating Explanation
The study presents a reasonable investigation of bias in LLMs using diverse methodologies, but suffers from limitations such as limited persona scope, noisy evaluation, and limited generalizability in the salary negotiation scenario. While the knowledge-based experiments show less clear bias, the salary advice experiment reveals more pronounced socio-economic biases, making this a valuable contribution. However, these limitations prevent a higher rating.
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Original Title:
Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models
File Name:
2506.10491v1.pdf
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File Size:
0.78 MB
Uploaded:
July 21, 2025 at 06:54 AM
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