@unpublished{wang2026hiring,title={Large Language Models in Hiring},author={Wang, Ze and Shen, Guobin and Thaler, Michael},year={2026},note={Work in progress},}
@unpublished{hernandez2026education,title={AI and Education},author={Vera Hernandez, Marcos and Giardili, Soledad and Wang, Ze},year={2026},note={Work in progress},}
Bias Amplification: Large Language Models as Increasingly Biased Media
Ze Wang, Zekun Wu, Yichi Zhang, and 5 more authors
In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL), 2025
Model collapse—a phenomenon where models degrade in performance due to indiscriminate use of synthetic data—is well studied. However, its role in bias amplification—the progressive reinforcement of pre-existing social biases in Large Language Models (LLMs)—remains underexplored. In this paper, we formally define the conditions for bias amplification and demonstrate through statistical simulations that bias can intensify even in the absence of sampling errors, the primary driver of model collapse. Empirically, we investigate political bias amplification in GPT-2 using a custom-built benchmark for sentence continuation tasks. Our findings reveal a progressively increasing right-leaning bias. Furthermore, we evaluate three mitigation strategies—Overfitting, Preservation, and Accumulation—and show that bias amplification persists even when model collapse is mitigated. Finally, a mechanistic interpretation identifies distinct sets of neurons responsible for model collapse and bias amplification, suggesting they arise from different underlying mechanisms.
@inproceedings{wang2025bias,title={Bias Amplification: Large Language Models as Increasingly Biased Media},author={Wang, Ze and Wu, Zekun and Zhang, Yichi and Guan, Xin and Jain, Navya and Lu, Qinyang and Gupta, Saloni and Koshiyama, Adriano},booktitle={Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL)},year={2025},}
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) for resume scoring, revealing significant issues of reverse gender hiring bias and overdebiasing. Our contributions are fourfold: Firstly, we introduce a new construct grounded in labour economics, legal principles, and critiques of current bias benchmarks: hiring bias can be categorized into two types: Level bias (difference in the average outcomes between demographic counterfactual groups) and Spread bias (difference in the variance of outcomes between demographic counterfactual groups); Level bias can be further subdivided into statistical bias (i.e. changing with non-demographic content) and taste-based bias (i.e. consistent regardless of non-demographic content). Secondly, the framework includes rigorous statistical and computational hiring bias metrics, such as Rank After Scoring (RAS), Rank-based Impact Ratio, Permutation Test, and Fixed Effects Model. Thirdly, we analyze gender hiring biases in ten state-of-the-art LLMs. Seven out of ten LLMs show significant biases against males in at least one industry. An industry-effect regression reveals that the healthcare industry is the most biased against males. Moreover, we found that the bias performance remains invariant with resume content for eight out of ten LLMs. This indicates that the bias performance measured in this paper might apply to other resume datasets with different resume qualities. Fourthly, we provide a user-friendly demo and resume dataset to support the adoption and practical use of the framework, which can be generalized to other social traits and tasks.
@inproceedings{wang2024jobfair,title={JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models},author={Wang, Ze and Wu, Zekun and Guan, Xin and Thaler, Michael and Koshiyama, Adriano and Lu, Skylar and Beepath, Sachin and Ertekin, Ediz and Perez-Ortiz, Maria},booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},year={2024},}