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嘉宾介绍：柯滨（Bin Ke）教授，美国密歇根州立大学博士，曾任教于宾夕法尼亚州立大学，新加坡南洋理工大学，现任新加坡国立大学商学院会计系教授、教务长。2010年获聘国家级重大人才工程。曾任北美华人会计教授会（颁础笔础狈础）会长。兼任The Accounting Review、Journal of American Taxation Association、The International Journal of Accounting编委。柯滨教授先后在The Accounting Review、Journal of Accounting and Economics、Journal of Accounting Research、Review of Accounting Studies和Contemporary Accounting Research上发表多篇学术论文。柯滨教授擅长从经济学的角度研究会计信息的生成与运用过程，主要研究领域包括盈余管理、内幕交易、机构投资者与财务分析师行为等，近期主要关注新兴市场（尤其是中国）的财务报告、管理层激励和投资者保护问题。
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论文摘要：Leveraging proprietary job application data from a financial institution, we investigate the efficacy of machine learning (ML) in enhancing employee selection processes. We demonstrate that ML models can not only emulate human recruiters' decisions in matching applicants with employers but also substantially reduce inefficiencies in these matches. Critically, our most effective machine learning models excel in identifying job candidates likely to demonstrate a lack of commitment to the firm, as well as those poised to deliver exceptional job performance. Employing Shapley values, we illuminate the distinctions in decision-making between human recruiters and our ML models. In addition, our topperforming ML models exhibit reduced biases compared to human recruiters, against disadvantaged groups. These insights emphasize the transformative role of machine learning in optimizing employee selection and enhancing management control strategies.
