普通人对公平、补救性与歧视性算法态度分化，尤其在政治立场和种族身份上存在显著差异。
Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms
- 通过两组实验（共1193人）调查公众对三类算法的态度。
- 多数人支持公平算法，反对歧视性算法；但对补救性算法看法两极分化。
- 保守派和主流群体更反感补救性算法，认知差异是分歧根源。
补救性算法作为应对算法歧视的潜在方案，旨在纠正历史不公并弥补过往伤害。我们通过两项实验（共1193人）研究了公众对补救性算法——即明确优先考虑历史上被边缘化群体的算法——在招聘与刑事司法场景中的看法。我们将这些观点与公众对偏袒特权群体（即歧视性）算法以及不考虑人口属性（即公平）算法的态度进行对比。结果发现，无论政治立场或身份背景如何，大多数人普遍支持公平算法，并谴责歧视性系统。然而，在补救性算法问题上存在分歧：自由派及少数族裔认为其与公平算法同样积极，而保守派及主流种族群体则将其视为与歧视性系统同等负面。我们识别出这一分歧的根源在于人们对谁（或是否有人）被边缘化的认知差异。文章探讨了弥合这些分歧的可能性，以推动更多人接受补救性算法。
原文摘要 · Abstract (English)
Affirmative algorithms have emerged as a potential answer to algorithmic discrimination, seeking to redress past harms and rectify the source of historical injustices. We present the results of two experiments ($N$$=$$1193$) capturing laypeople's perceptions of affirmative algorithms -- those which explicitly prioritize the historically marginalized -- in hiring and criminal justice. We contrast these opinions about affirmative algorithms with folk attitudes towards algorithms that prioritize the privileged (i.e., discriminatory) and systems that make decisions independently of demographic groups (i.e., fair). We find that people -- regardless of their political leaning and identity -- view fair algorithms favorably and denounce discriminatory systems. In contrast, we identify disagreements concerning affirmative algorithms: liberals and racial minorities rate affirmative systems as positively as their fair counterparts, whereas conservatives and those from the dominant racial group evaluate affirmative algorithms as negatively as discriminatory systems. We identify a source of these divisions: people have varying beliefs about who (if anyone) is marginalized, shaping their views of affirmative algorithms. We discuss the possibility of bridging these disagreements to bring people together towards affirmative algorithms.
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