论文标题
组织中更公平的机器学习框架
A Framework for Fairer Machine Learning in Organizations
论文作者
论文摘要
随着组织对机器学习工具的采用增加，不公平的风险比比皆是，尤其是当人类决策过程中的社会经济重要性结果（例如雇用，住房，贷款和招生）是自动化的。我们揭示了不公平的机器学习的来源，审查公平性标准，并提供一个框架，如果实施，该框架将使组织既可以避免实施不公平的机器学习模型，又要避免随着算法学习更多数据而变得不公平的常见情况。文献中尚未彻底解决组织机器学习实施中的行为伦理问题，因为许多必要的概念都分散在三种文献中：伦理，机器学习和管理。此外，机器学习中的公平标准之间的权衡并未在组织方面解决。我们通过引入一个组织框架来选择和实施组织中的公平算法来推进研究。
With the increase in adoption of machine learning tools by organizations risks of unfairness abound, especially when human decision processes in outcomes of socio-economic importance such as hiring, housing, lending, and admissions are automated. We reveal sources of unfair machine learning, review fairness criteria, and provide a framework which, if implemented, would enable an organization to both avoid implementing an unfair machine learning model, but also to avoid the common situation that as an algorithm learns with more data it can become unfair over time. Issues of behavioral ethics in machine learning implementations by organizations have not been thoroughly addressed in the literature, because many of the necessary concepts are dispersed across three literatures: ethics, machine learning, and management. Further, tradeoffs between fairness criteria in machine learning have not been addressed with regards to organizations. We advance the research by introducing an organizing framework for selecting and implementing fair algorithms in organizations.
