A Qualitative Exploration of Perceptions of Algorithmic Fairness
A Qualitative Exploration of Perceptions of Algorithmic Fairness
复制标题
对算法公平性认知的定性探索
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Warshaw
中科院分区：
文献类型：
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作者：
Allison Woodruff;Sarah E. Fox;Steven Rousso;J. Warshaw
算法系统越来越多地塑造人们所接触的信息，并影响有关就业、财务和其他机会的决策。在某些情况下，算法系统可能或多或少对某些群体或个人有利，在公共政策界、学术界和新闻界引发了关于算法公平性的实质性讨论。我们通过探索潜在受影响社区的成员对算法公平性的感受来扩大这一讨论。我们举办了研讨会，并采访了44名参与者，他们来自美国几个传统上被种族或阶级类别边缘化的人群。虽然算法公平的概念在很大程度上是陌生的，但学习算法公平引发了负面情绪，这些情绪与当前关于种族不公正和经济不平等的全国性讨论有关。除了担心对自己和社会的潜在危害外，参与者还表示，算法公平(或缺乏公平)可能会极大地影响他们对公司或产品的信任。
Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.
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：
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作者：
Lee, Min Kyung;Kim, Ji Tae;Lizarondo, Leah
通讯作者：
Lizarondo, Leah