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Expanding Explainability: Towards Social Transparency in AI systems

arXiv 2101.04719 ↗First posted 12 Jan 2021cs.HC cs.AIcited by 124

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Abstract (arXiv)

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.

Key facts. Cited by 124 papers in the map (data through 7 Oct 2026). Builds on 24 of its 119 references that are in the map. First posted on arXiv 12 Jan 2021.

Builds on

24 of 119 references.

References · 24

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2016The Mythos of Model Interpretabilitycited by 716
  5. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  6. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  7. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  8. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  9. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  10. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  11. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  12. 2018Explaining Explanations in AIcited by 136
12 more
  1. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
  2. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  3. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
  4. 2020Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Studycited by 86
  5. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
  6. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 73
  7. 2019explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learningcited by 69
  8. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  9. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
  10. 2018The Challenge of Crafting Intelligible Intelligencecited by 46
  11. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  12. 2018TED: Teaching AI to Explain its Decisionscited by 33

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Citing papers per year: 2021: 8; 2022: 23; 2023: 25; 2024: 29; 2025: 21; 2026: 18.

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  1. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
  2. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  3. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  4. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  5. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  6. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  7. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  8. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  9. 2024Design Principles for Generative AI Applicationscited by 57
  10. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  11. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  12. 2023Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systemscited by 40
38 more
  1. 2021Explainability Pitfalls: Beyond Dark Patterns in Explainable AIcited by 36
  2. 2023Writer-Defined AI Personas for On-Demand Feedback Generationcited by 36
  3. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  4. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  5. 2025Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Supportcited by 33
  6. 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
  7. 2022Causal Proxy Models for Concept-Based Model Explanationscited by 29
  8. 2021Towards Relatable Explainable AI with the Perceptual Processcited by 27
  9. 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 26
  10. 2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
  11. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  12. 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
  13. 2023Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systemscited by 20
  14. 2025As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Makingcited by 18
  15. 2023XAIR: A Framework of Explainable AI in Augmented Realitycited by 17
  16. 2022Mutual Theory of Mind for Human-AI Communicationcited by 15
  17. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  18. 2024Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Surveycited by 15
  19. 2025The challenge of uncertainty quantification of large language models in medicinecited by 14
  20. 2022Mitigating Covertly Unsafe Text within Natural Language Systemscited by 13
  21. 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Makingcited by 11
  22. 2022The Algorithmic Imprintcited by 10
  23. 2022Human-Centric Research for NLP: Towards a Definition and Guiding Questionscited by 9
  24. 2022Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcarecited by 9
  25. 2025Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisionscited by 9
  26. 2023Human-Centered Responsible Artificial Intelligence: Current & Future Trendscited by 8
  27. 2023A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depressioncited by 8
  28. 2024Rethinking Explainable Machine Learning as Applied Statisticscited by 8
  29. 2024Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaborationcited by 8
  30. 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
  31. 2024'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMscited by 7
  32. 2024Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapycited by 7
  33. 2022Social Construction of XAI: Do We Need One Definition to Rule Them All?cited by 6
  34. 2023Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Developmentcited by 6
  35. 2025Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoMLcited by 6
  36. 2025Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articlescited by 6
  37. 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interactioncited by 6
  38. 2021A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interactioncited by 5

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