Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms
This research addresses the challenge of public acceptance for affirmative algorithms, which aim to reduce algorithmic discrimination, but reveals deep societal divisions based on political and identity factors, making it incremental in understanding attitudes rather than proposing technical solutions.
The study investigated laypeople's perceptions of affirmative, fair, and discriminatory algorithms in hiring and criminal justice, finding that while all groups favor fair algorithms and denounce discriminatory ones, liberals and racial minorities view affirmative algorithms positively, whereas conservatives and dominant racial groups view them as negatively as discriminatory systems.
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.