JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models (2406.15484v2)

Abstract: The use of 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 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.

Analysis of "JobFair: A Framework for Benchmarking Gender Hiring Bias in LLMs"

The paper "JobFair: A Framework for Benchmarking Gender Hiring Bias in LLMs" presents a comprehensive framework for assessing gender bias in LLMs used for resume scoring. The authors develop a nuanced approach to discern the presence and type of hiring biases, specifically focusing on Level bias and Spread bias, and further distinguishing between Taste-based and Statistical biases. This paper is critical in the context of ethical AI development, particularly in high-stakes areas such as hiring, where bias can perpetuate systemic inequalities.

Contributions and Methodology

The authors' primary contributions are manifold. They propose a hierarchical construct of hiring bias grounded in labor economics and legal principles, categorizing biases into Level and Spread biases. Level bias is further divided into statistical and taste-based biases. This framework is operationalized through a methodology that emphasizes comprehensive statistical and computational metrics tailored to capture these biases in LLMs. The authors employ Rank After Scoring (RAS), Permutation Tests, and Fixed Effects Models, offering a robust statistical apparatus to their analysis.

A dataset consisting of 300 real resumes, carefully curated and anonymized, serves as the basis for their empirical analysis across three industries—healthcare, finance, and construction—chosen for their varying gender representations. The approach involves creating counterfactual gender versions of each resume, yielding rigorous insights into bias dynamics. This counterfactual method stands in contrast to name-based approaches in previous studies, which can conflate multiple social cues conveyed by names alone.

Findings

The results point to significant gender biases across most evaluated LLMs, typically against male applicants. Seven out of ten models display a statistically significant Level bias across at least one industrial sector, with no evidence of Spread bias. Interestingly, the healthcare sector appears particularly biased against males, a finding that aligns with global gender representation in this field. The consistent presence of Taste-based bias, as evidenced by fixed-effects model results, suggests these biases are ingrained and unchanged by variations in resume length.

Implications for AI Development

The implications of this research are profound, especially as LLMs become more integrated into automated decision-making processes. The demonstration of systematic biases, even in state-of-the-art models from major AI developers, underscores the need for improved bias detection and mitigation techniques in AI systems. Additionally, the paper stresses the limitations of traditional bias measurement techniques, such as the Four-fifths rule, advocating for more sensitive statistical tests that can reduce Type II errors.

Moreover, the distinction between Taste-based and Statistical biases not only provides insight into the nature of bias embedded within LLMs but also suggests that certain biases may be more resistant to mitigation efforts—those grounded in taste or preference rather than information deficiency, for instance.

Future Directions

The framework presented offers a springboard for future research. As AI systems evolve, the proposed methodologies could be extended beyond gender, encompassing other demographic biases such as race, age, or socioeconomic status. Furthermore, the insights gained here could inform the development of legislation and corporate guidelines aiming to ensure equitable AI practices, highlighting the importance of continuous, rigorous bias auditing in AI tools.

The JobFair framework also poses significant questions regarding the adaptability of LLMs when trained or retrained with bias-aware datasets or methods. Understanding how models might evolve with targeted interventions or enhanced data is critical to reducing biases.

In conclusion, this paper makes a substantial contribution to the discourse on ethical AI use. It provides a well-structured methodological framework for exploring gender bias in LLM-based evaluations, emphasizing the role of robust statistical analyses. The insights necessitate ongoing attention to how AI is leveraged in hiring and other domains involving critical human-centered decisions.