论文标题
影响算法决策中感知公平性的因素：算法结果，发展程序和个体差异
Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
论文作者
论文摘要
算法决策系统越来越多地在整个公共和私营部门中使用，以做出重要的决策或帮助人类制定真正的社会后果决策。尽管近年来已经进行了大量研究来建立公平的决策算法，但较少的研究试图了解影响人们对这些系统公平性看法的因素，我们认为这对于他们更广泛的接受也很重要。在这项研究中，我们进行了在线实验，以更好地了解公平性的看法，重点关注三组因素：算法结果，算法开发和部署程序以及个体差异。我们发现，当算法预测其有利的算法，甚至超过描述对特定人群群体非常有偏见的算法的负面影响时，人们对算法的评价更为公平。我们发现，这种效果受到了几个变量的调节，包括参与者的教育水平，性别以及发展程序的几个方面。我们的发现表明，通过用户反馈评估算法公平性的系统必须考虑结果有利性偏见的可能性。
Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of outcome favorability bias.
