Social Bias in Language Models

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13 papers in the last four weeks, up 44% on the four weeks before. 0.1% of all new papers.

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FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks
Jan 9, 2025Yongkang Du, Jen-tse Huang, Jieyu Zhao +1Social Bias in Language Models Algorithmic Fairness
Pennsylvania State University · University of Southern California
Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters
Jan 4, 2025Messi H. J. LeeSocial Bias in Language Models Open-Weight Language Models
Independent Researcher · Seoul, Republic of Korea
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Aug 20, 2024Yongxin Deng, Xiaoyu Tan, Jing Pan +3Social Bias in Language Models Language Model Bias Evaluation
University of Technology Sydney · School of Computer Science, University of Technology Sydney, Sydney, NSW 2007, Australia · National University of Singapore +3
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
Aug 8, 2024Yupeng Chang, Yi Chang, Yuan WuSocial Bias in Language Models Low-Rank Adaptation
School of Artificial Intelligence, Jilin University · Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University · International Center of Future Science, Jilin University
General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level
Nov 23, 2023Bingkang Shi, Xiaodan Zhang, Dehan Kong +4Gender Bias in Language Models Debiased ML
Chinese Academy of Sciences, Institute of Information Engineering, Beijing 100093, China · Alibaba Group, Alibaba Artificial Intelligence Governance Laboratory, Beijing, China · University of Bristol, Department of Electrical and Electronic Engineering, Bristol, BS8 1UB, UK
Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study
Date pendingNour Bouchouchi, Thibault Laugel, Xavier Renard +3Gender Bias in Language Models LLM Alignment
Sorbonne Université, CNRS, LIP6, F-75005 Paris, France · AXA, Paris, France · Polish Academy of Science, IBS PAN, Warsaw, Poland

Jan 9, 2025, cs.CLJ/K move · Enter open · S save

FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks

Yongkang Du, Jen-tse Huang, Jieyu Zhao, Lu Lin

Pennsylvania State University · University of Southern California

Social Bias in Language Models Algorithmic Fairness LLM Decision-Making Language Model Bias Evaluation

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Large language models (LLMs) are increasingly used in high-stakes decisions such as hiring and college admissions, making their social bias a critical concern. While LLMs are trained to refuse explicitly biased requests, bias can be leaked implicitly during LLM planning and reasoning process. As code becomes the primary medium for LLM internal logic-writing, we introduce FairCoder, a benchmark that frames decision-making as coding tasks to systematically probe LLM bias across employment, education, and healthcare domains, covering multiple fairness definitions. Considering that existing metrics may fail when LLMs frequently refuse the request, we propose FairScore, a metric that jointly captures refusal behavior and group-level outcome diversity. Experiments with a 1k-sample dataset on powerful LLMs reveal consistent and previously underexplored bias patterns, such as prioritizing applicants from high-income families in college admissions. Our findings highlight the risks of deploying LLMs as decision-making agents and provide a comprehensive evaluation framework for future research.

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Latest papers 126

FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models

BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models

General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level

Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study