mlsanityData to 7 Oct 2026

Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

arXiv 2103.12016 ↗First posted 22 Mar 2021cs.HC cs.AI cs.CYcited by 27

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 25 of its 132 references that are in the map. First posted on arXiv 22 Mar 2021.

Builds on

25 of 132 references.

References · 25

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2017Counterfactual Fairnesscited by 802
  3. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  4. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  5. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  6. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  7. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
  8. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  9. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  10. 2017On Formalizing Fairness in Prediction with Machine Learningcited by 65
  11. 2017From Parity to Preference-based Notions of Fairness in Classificationcited by 60
  12. 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
13 more
  1. 2018How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairnesscited by 48
  2. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  3. 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
  4. 2018Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Predictioncited by 35
  5. 2019Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgmentcited by 31
  6. 2021Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systemscited by 27
  7. 2020Dimensions of Diversity in Human Perceptions of Algorithmic Fairnesscited by 13
  8. 2021A Study on Fairness and Trust Perceptions in Automated Decision Makingcited by 7
  9. 2019Measuring Non-Expert Comprehension of Machine Learning Fairness Metricscited by 4
  10. 2020The Threats of Artificial Intelligence Scale (TAI). Development, Measurement and Test Over Three Application Domainscited by 4
  11. 2020Exploring User Opinions of Fairness in Recommender Systemscited by 3
  12. 2021The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairnesscited by 1
  13. 2021Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approachescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 12; 2025: 2; 2026: 5.

27 papers.

Sort:

  1. 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
  2. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  3. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  4. 2024ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methodscited by 8
  5. 2024Local Causal Discovery for Structural Evidence of Direct Discriminationcited by 6
  6. 2022On the Relationship Between Explanations, Fairness Perceptions, and Decisionscited by 3
  7. 2022Artificial intelligence in government: Concepts, standards, and a unified frameworkcited by 3
  8. 2024Personalized Help for Optimizing Low-Skilled Users' Strategycited by 3
  9. 2023Fair Enough? A map of the current limitations of the requirements to have fair algorithmscited by 2
  10. 2023Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnairescited by 1
  11. 2023Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questionscited by 1
  12. 2024Fairness Certification for Natural Language Processing and Large Language Modelscited by 1
15 more
  1. 2024Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policiescited by 1
  2. 2024Mapping the Potential of Explainable AI for Fairness Along the AI Lifecyclecited by 1
  3. 2024Challenging the Human-in-the-loop in Algorithmic Decision-makingcited by 1
  4. 2024From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?cited by 1
  5. 2025A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systemscited by 1
  6. 2026Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Modelscited by 1
  7. 2026"Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage Systemcited by 1
  8. 2024Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Productioncited by 0
  9. 2024Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristicscited by 0
  10. 2024Revisiting Technical Bias Mitigation Strategiescited by 0
  11. 2024Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?cited by 0
  12. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  13. 2026Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systemscited by 0
  14. 2026From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Healthcited by 0
  15. 2026Can LLMs Rank? A Tale of Triads and Triagecited by 0