Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Algorithmic decision-making 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 considering people's fairn...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-07-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/20539517221115189 |
| _version_ | 1817984199664074752 |
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| author | Christopher Starke Janine Baleis Birte Keller Frank Marcinkowski |
| author_facet | Christopher Starke Janine Baleis Birte Keller Frank Marcinkowski |
| author_sort | Christopher Starke |
| collection | DOAJ |
| description | Algorithmic decision-making 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 considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. 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 algorithmic decision-making. |
| first_indexed | 2024-04-13T23:42:35Z |
| format | Article |
| id | doaj.art-5d1ca7e59b8b416cad60342518c72507 |
| institution | Directory Open Access Journal |
| issn | 2053-9517 |
| language | English |
| last_indexed | 2024-04-13T23:42:35Z |
| publishDate | 2022-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj.art-5d1ca7e59b8b416cad60342518c725072022-12-22T02:24:27ZengSAGE PublishingBig Data & Society2053-95172022-07-01910.1177/20539517221115189Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literatureChristopher Starke0Janine Baleis1Birte Keller2Frank Marcinkowski3 Amsterdam School of Communication Research, , Amsterdam, the Netherlands Department of Social Sciences, , Düsseldorf, Germany Department of Social Sciences, , Düsseldorf, Germany Department of Social Sciences, , Düsseldorf, GermanyAlgorithmic decision-making 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 considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. 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 algorithmic decision-making.https://doi.org/10.1177/20539517221115189 |
| spellingShingle | Christopher Starke Janine Baleis Birte Keller Frank Marcinkowski Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature Big Data & Society |
| title | Fairness perceptions of algorithmic decision-making: A systematic review
of the empirical literature |
| title_full | Fairness perceptions of algorithmic decision-making: A systematic review
of the empirical literature |
| title_fullStr | Fairness perceptions of algorithmic decision-making: A systematic review
of the empirical literature |
| title_full_unstemmed | Fairness perceptions of algorithmic decision-making: A systematic review
of the empirical literature |
| title_short | Fairness perceptions of algorithmic decision-making: A systematic review
of the empirical literature |
| title_sort | fairness perceptions of algorithmic decision making a systematic review of the empirical literature |
| url | https://doi.org/10.1177/20539517221115189 |
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