  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.
