AI employment decision-making: Integrating the equal opportunity merit principle and explainable AI

• Main Author: CHAN, Gary Kok Yew
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Artificial intelligence; Employment decision-making; Bias; Fairness; Equal opportunity; Merit; Explainable AI; Artificial Intelligence and Robotics; Labor and Employment Law
• Online Access: https://ink.library.smu.edu.sg/sol_research/4518; https://ink.library.smu.edu.sg/context/sol_research/article/6476/viewcontent/s00146_022_01532_w.pdf
• Institution: Singapore Management University

Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of reasonable users (employing organisations) and affected persons (employees and job candidates) as to the AI output. Both the equal opportunity merit principle and explainable AI should be integrated in the design and implementation of AI employment decision-making systems so as to ensure, as far as possible, that the AI output is arrived at through a fair process.