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.

中文翻译：

---

使用机器学习选择董事

算法可以帮助公司决定提名公司董事吗？与样本外测试中的实际候选人库相比，算法预测的表现不佳的董事确实表现不佳。可以预见，糟糕的导演更有可能是男性，积累了更多的董事职位，并且拥有比算法推荐的董事更大的网络。治理结构较弱的公司更有可能提名他们。我们的研究结果表明，机器学习有望帮助理解选择治理结构的过程，并有可能帮助现实世界的公司改善治理。