Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Abstract

There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.

References in corpus (4)

• FA*IR: A Fair Top-k Ranking Algorithm
• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• Problem Formulation and Fairness

Cited by in corpus (50)

• Fairness in Machine Learning: A Survey
• Power to the People? Opportunities and Challenges for Participatory AI
• Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
• The Fallacy of AI Functionality
• Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
• Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
• Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries
• The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness
• The Threats of Artificial Intelligence Scale (TAI). Development, Measurement and Test Over Three Application Domains
• Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
• A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
• Black-Box Access is Insufficient for Rigorous AI Audits
• Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment
• On the Moral Justification of Statistical Parity
• Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies
• Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
• Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention
• The Algorithmic Imprint
• The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring
• Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
• Explainable Natural Language Processing for Corporate Sustainability Analysis
• Mimetic Models: Ethical Implications of AI that Acts Like You
• Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions
• FairCanary: Rapid Continuous Explainable Fairness
• On Fair Selection in the Presence of Implicit Variance
• Algorithmic Pluralism: A Structural Approach To Equal Opportunity
• Examining risks of racial biases in NLP tools for child protective services
• "Finding the Magic Sauce": Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools
• Setting the Right Expectations: Algorithmic Recourse Over Time
• On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
• Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy
• Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
• Disentangling and Operationalizing AI Fairness at LinkedIn
• Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox
• Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
• Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers
• Leave no Place Behind: Improved Geolocation in Humanitarian Documents
• Strategic Evaluation: Subjects, Evaluators, and Society
• What Constitutes a Less Discriminatory Algorithm?
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
• Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems
• Locating disparities in machine learning
• Towards a multi-stakeholder value-based assessment framework for algorithmic systems
• A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
• Algorithms in the Stacks: Investigating automated, for-profit diversity audits in public libraries
• Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach
• Addressing Bias in Online Selection with Limited Budget of Comparisons
• Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems