Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
复制标题
影响算法决策中感知公平性的因素：算法结果、开发程序和个体差异
DOI：
10.1145/3313831.3376813
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Zhu, Haiyi
中科院分区：
文献类型：
--
作者：
Wang, Ruotong;Harper, F. Maxwell;Zhu, Haiyi
公共和私营部门越来越多地使用军事决策系统来作出重要决定或协助人们作出具有真实的社会后果的决定。虽然近年来有大量的研究来建立公平的决策算法，但很少有研究试图了解影响人们对这些系统中公平性的看法的因素，我们认为这对他们更广泛的接受也很重要。在这项研究中，我们进行了一项在线实验，以更好地了解公平的看法，重点是三组因素：算法结果，算法开发和部署程序，以及个体差异。我们发现，当算法预测对他们有利时，人们会认为算法更公平，甚至超过了描述对特定人口群体非常有偏见的算法的负面影响。我们发现，这种影响是由几个变量，包括参与者的教育水平，性别和发展过程的几个方面的调节。我们的研究结果表明，通过用户反馈评估算法公平性的系统必须考虑“结果可验证性”偏差的可能性。
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.
登录
查看更多内容
以人为本的算法服务：向非营利组织分配捐款的公平和激励智能社区服务管理的考虑
DOI：
10.1145/3025453.3025884
发表时间：
2017
期刊：
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
影响因子：
--
作者：
Lee, Min Kyung;Kim, Ji Tae;Lizarondo, Leah
通讯作者：
Lizarondo, Leah
未知
DOI：
--
发表时间：
2019
期刊：
The Future of Reading
影响因子：
--
作者：
E. Purchase
通讯作者：
E. Purchase
感知的正义和对强制计算机的反应
DOI：
--
发表时间：
2012
期刊：
影响因子：
--
作者：
Daniel B. Shank
通讯作者：
Daniel B. Shank
从技术、监控和隐私角度看移民警务和联邦制
DOI：
--
发表时间：
2013
期刊：
影响因子：
--
作者：
Anil Kalhan
通讯作者：
Anil Kalhan
公众同情心的意识形态和归因界限：对受自然灾害影响的个人和社区的反应
影响因子：
4
作者：
L. Skitka
通讯作者：
L. Skitka