Selecting Directors Using Machine Learning
研究发现，算法预测表现不佳的董事在实际中确实表现较差，且更可能是男性、拥有更多董事职位和更大社交网络，公司治理较弱的企业更可能提名这类董事。
Abstract Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that machine learning holds promise for understanding the process by which governance structures are chosen and has potential to help real-world firms improve their governance.