算法时代的公平招聘
Hiring Fairly in the Age of Algorithms
摘要：
自动化的广泛发展减少了对人力投入的需求。然而，尽管机器学习的能力越来越强，但在许多情况下，这些程序做出的决策是有问题的。数据中的偏见和不透明的模型放大了人类的偏见，催生了亚马逊（现已停产）实验性招聘算法等工具，该算法被发现在一项活动之前添加“女性”一词时，会持续降低简历的等级。本文批判性地调查了围绕算法招聘的现有法律和技术环境。我们认为，招聘算法的负面影响可以通过提高雇主对公众的透明度来减轻，这将使民事倡导团体能够追究雇主的责任，并允许美国司法部提起诉讼。我们的主要贡献是自动招聘透明度框架，算法透明度报告，使用自动招聘软件的雇主必须依法发布。我们还解释了如何在就业和商业秘密法的现有法规可以由平等就业机会委员会和国会扩展，以适应这些报告。
英文摘要：
Widespread developments in automation have reduced the need for human input. However, despite the increased power of machine learning, in many contexts these programs make decisions that are problematic. Biases within data and opaque models have amplified human prejudices, giving rise to such tools as Amazon's (now defunct) experimental hiring algorithm, which was found to consistently downgrade resumes when the word "women's" was added before an activity. This article critically surveys the existing legal and technological landscape surrounding algorithmic hiring. We argue that the negative impact of hiring algorithms can be mitigated by greater transparency from the employers to the public, which would enable civil advocate groups to hold employers accountable, as well as allow the U.S. Department of Justice to litigate. Our main contribution is a framework for automated hiring transparency, algorithmic transparency reports, which employers using automated hiring software would be required to publish by law. We also explain how existing regulations in employment and trade secret law can be extended by the Equal Employment Opportunity Commission and Congress to accommodate these reports.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
