Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms

TL;DR

This study investigates laypeople's perceptions of affirmative algorithms in hiring and criminal justice, revealing political and racial divides in attitudes towards these systems and highlighting the influence of beliefs about marginalization.

Contribution

It provides empirical insights into public opinions on affirmative algorithms, contrasting them with fair and discriminatory systems, and explores factors influencing these perceptions.

Findings

Fair algorithms are generally viewed positively.

Disagreements exist based on political and racial identities.

Beliefs about who is marginalized influence attitudes.

Abstract

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.