Building and Auditing Fair Algorithms - A Case Study in Candidate Screening

Explore a 20-minute conference talk from the FAccT 2021 virtual event that delves into the critical topic of algorithmic fairness in candidate screening. Gain insights from a comprehensive case study presented by researchers C. Wilson, A. Ghosh, S. Jiang, A. Mislove, L. Baker, J. Szary, K. Trindel, and F. Polli. Examine the challenges and methodologies involved in constructing and evaluating fair algorithms within the context of recruitment processes. Learn about the implications of algorithmic decision-making in hiring practices and the importance of auditing these systems for potential biases. Discover how researchers approach the complex task of balancing efficiency and fairness in automated candidate selection. Access this thought-provoking presentation through the Association for Computing Machinery (ACM) Digital Library to enhance your understanding of ethical considerations in AI-driven hiring technologies.

Building and Auditing Fair Algorithms: A Case Study in Candidate Screening