Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Max Planck Institute for Security and Privacy（安全与隐私研究所）；Radboud University（拉德博德大学）；Eindhoven University of Technology（埃因霍温理工大学）；Randstad（Randstad公司）；European University Viadrina（弗赖堡大学）
Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, Asia J. Biega
备注：
Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, and Asia J. Biega. Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey. ACM Transactions on Intelligent Systems and Technology. 2025. https://doi.org/10.1145/3696457
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.
Towards clinical AI fairness: A translational perspective
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Antwerp（安特卫普大学）；University of Florida（佛罗里达大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；SingHealth AI Office（新加坡保健服务集团AI办公室）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；Weill Cornell Medicine（威尔康奈尔医学院）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；National University of Singapore（新加坡国立大学）
本文从转化医学视角分析 AI 公平性在医疗场景中技术与临床认知的错位及落地障碍，主张多学科协作并提出应对临床公平性问题的解决方案。
With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a "fair" ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute "fairness". The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs t…
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license {https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithm…
University of Michigan（密歇根大学）；Western University（韦仕敦大学）
Existing efforts to formulate computational definitions of fairness have largely focused on distributional notions of equality, where equality is defined by the resources or decisions given to individuals in the system. Yet existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources. Here, we show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations. To do this, we provide an example of a self-confirming equilibrium in a simple hiring marke…
Politecnico di Torino（都灵理工大学）；Istituto Nazionale di Ricerca Metrologica（国家计量研究院）
Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life. AI is clearly supportive in many decision-making scenarios, but when it comes to sensitive areas such as health care, hiring policies, education, banking or justice, with major impact on individuals and society, it becomes crucial to establish guidelines on how to design, develop, deploy and monitor this technology. Indeed the decision rules elaborated by machine learning models a…
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing t…
Small Changes, Big Impact: Demographic Bias in LLM-Based Hiring Through Subtle Sociocultural Markers in Anonymised Resumes
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；GovTech Singapore（新加坡政府科技局）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Agency for Science, Technology and Research (A*STAR)（科学技术研究局）；University of British Columbia (UBC)（不列颠哥伦比亚大学）
AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
Robert H. Smith School of Business, University of Maryland（马里兰大学罗伯特H史密斯商学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Max M. Fisher College of Business, The Ohio State University（俄亥俄州立大学马克M费舍尔商学院）
The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；New York University（纽约大学）；University of Surrey（萨里大学）；University College London（伦敦大学学院）
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal in…
University of Michigan（密歇根大学）；Michigan Tech Research Institute（密歇根理工大学研究所）；University of Utah（犹他大学）
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we call a pipeline. Prior work in algorithmic fairness only focuses on fairness with respect to one decision. However, many decision-making processes require more than one decision. For instance, hiring is at least a two stage model: deciding who to interview from the applicant pool and then deciding who to hire from the interview pool. Perhaps surpr…
Virginia Commonwealth University（弗吉尼亚联邦大学）；University of Notre Dame（圣母大学）
Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of corporations. This posits the requirement of model fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race, or age) that are often under-represented in the data. We postulate that this problem of under-representation has a corollary to the problem of imbalanced data learning. This class…
With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to repre…
