mlsanityData to 14 Oct 2026

Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness

arXiv 2206.00474 ↗First posted 1 Jun 2022cs.AI cs.HCcited by 9

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Abstract (arXiv)

With Artificial intelligence (AI) to aid or automate decision-making advancing rapidly, a particular concern is its fairness. In order to create reliable, safe and trustworthy systems through human-centred artificial intelligence (HCAI) design, recent efforts have produced user interfaces (UIs) for AI experts to investigate the fairness of AI models. In this work, we provide a design space exploration that supports not only data scientists but also domain experts to investigate AI fairness. Using loan applications as an example, we held a series of workshops with loan officers and data scientists to elicit their requirements. We instantiated these requirements into FairHIL, a UI to support human-in-the-loop fairness investigations, and describe how this UI could be generalized to other use cases. We evaluated FairHIL through a think-aloud user study. Our work contributes better designs to investigate an AI model's fairness-and move closer towards responsible AI.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 22 of its 77 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

22 of 77 references.

References · 22

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  5. 2017Counterfactual Fairnesscited by 804
  6. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  7. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  8. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  9. 2018A Reductions Approach to Fair Classificationcited by 587
  10. 2017Algorithmic decision making and the cost of fairnesscited by 403
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
  12. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
10 more
  1. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  2. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
  3. 2019FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learningcited by 93
  4. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  5. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
  6. 2019FairSight: Visual Analytics for Fairness in Decision Makingcited by 59
  7. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  8. 2019Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgmentcited by 31
  9. 2021Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systemscited by 27
  10. 2020Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivismcited by 11

Built on by

Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  2. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  3. 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AIcited by 3
  4. 2023Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systemscited by 2
  5. 2024Ethical AI Governance: Methods for Evaluating Trustworthy AIcited by 2
  6. 2025Towards Multi-Stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-matching Systemcited by 1
  7. 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
  8. 2026Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Toolscited by 0
  9. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0