mlsanityData to 7 Oct 2026

Towards Involving End-users in Interactive Human-in-the-loop AI Fairness

arXiv 2204.10464 ↗First posted 22 Apr 2022cs.HC cs.AIcited by 12

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Abstract (arXiv)

Ensuring fairness in artificial intelligence (AI) is important to counteract bias and discrimination in far-reaching applications. Recent work has started to investigate how humans judge fairness and how to support machine learning (ML) experts in making their AI models fairer. Drawing inspiration from an Explainable AI (XAI) approach called explanatory debugging used in interactive machine learning, our work explores designing interpretable and interactive human-in-the-loop interfaces that allow ordinary end-users without any technical or domain background to identify potential fairness issues and possibly fix them in the context of loan decisions. Through workshops with end-users, we co-designed and implemented a prototype system that allowed end-users to see why predictions were made, and then to change weights on features to "debug" fairness issues. We evaluated the use of this prototype system through an online study. To investigate the implications of diverse human values about fairness around the globe, we also explored how cultural dimensions might play a role in using this prototype. Our results contribute to the design of interfaces to allow end-users to be involved in judging and addressing AI fairness through a human-in-the-loop approach.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 21 of its 88 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

21 of 88 references.

References · 21

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  5. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  6. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  7. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  8. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  9. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  10. 2017Fairness Testing: Testing Software for Discriminationcited by 123
  11. 2019Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Makingcited by 109
  12. 2019FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learningcited by 93
9 more
  1. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  2. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  3. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
  4. 2019FairSight: Visual Analytics for Fairness in Decision Makingcited by 59
  5. 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
  6. 2018How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairnesscited by 48
  7. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  8. 2020Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivismcited by 11
  9. 2020Joint Optimization of AI Fairness and Utility: A Human-Centered Approachcited by 10

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Cited by year

Citing papers per year: 2023: 7; 2024: 5.

12 papers.

  1. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  2. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  3. 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AIcited by 3
  4. 2023Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligencecited by 2
  5. 2023Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systemscited by 2
  6. 2023Improving Fairness in Adaptive Social Exergames via Shapley Banditscited by 1
  7. 2024Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systemscited by 1
  8. 2024Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Existcited by 1
  9. 2023Beyond XAI:Obstacles Towards Responsible AIcited by 0
  10. 2024Design Requirements for Human-Centered Graph Neural Network Explanationscited by 0
  11. 2024Noise-Free Explanation for Driving Action Predictioncited by 0
  12. 2024TA3: Testing Against Adversarial Attacks on Machine Learning Modelscited by 0