mlsanityData to 7 Oct 2026

Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance

arXiv 2207.01482 ↗First posted 10 Jun 2022cs.CY cs.HC cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a novel stakeholder-first approach that assists technologists in designing transparent, regulatory compliant systems. We also describe a real-world case-study that illustrates how this approach can be used in practice.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 100 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

31 of 100 references.

References · 31

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  5. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  6. 2016The Mythos of Model Interpretabilitycited by 716
  7. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  8. 2018Actionable Recourse in Linear Classificationcited by 327
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  10. 2019Explainable Machine Learning in Deploymentcited by 210
  11. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
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19 more
  1. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  2. 2020One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparencycited by 64
  3. 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 63
  4. 2020Interpretability and Explainability: A Machine Learning Zoo Mini-tourcited by 55
  5. 2018Stakeholders in Explainable AIcited by 48
  6. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  7. 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
  8. 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 38
  9. 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
  10. 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"cited by 26
  11. 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
  12. 2019Defining AI in Policy versus Practicecited by 15
  13. 2017Simple rules for complex decisionscited by 14
  14. 2018Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?cited by 14
  15. 2019What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiringcited by 14
  16. 2020Designing for Human Rights in AIcited by 13
  17. 2021Towards Accountability in the Use of Artificial Intelligence for Public Administrationscited by 12
  18. 2020Fair Machine Learning Under Partial Compliancecited by 5
  19. 2020Participatory Design to build better contact- and proximity-tracing appscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 2.

7 papers.

  1. 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
  2. 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directionscited by 5
  3. 2025"Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroomcited by 2
  4. 2025Investigation of the Privacy Concerns in AI Systems for Young Digital Citizens: A Comparative Stakeholder Analysiscited by 1
  5. 2023Does Explainable AI Have Moral Value?cited by 0
  6. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 0
  7. 2026AI Transparency: Governance Compliance or Stakeholder Requirements?cited by 0