Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature
This work addresses fairness concerns in algorithmic decision-making for researchers and policymakers by highlighting gaps in empirical literature, though it is incremental as a review.
The authors conducted a systematic review of 39 empirical studies to synthesize insights on fairness perceptions of algorithmic decision-making, identifying heterogeneity in concepts and measurements but noting that findings are largely limited to Western-democratic contexts.
Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible ADM.