mlsanityData to 7 Oct 2026

Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory

arXiv 2205.05057 ↗First posted 10 May 2022cs.HC cs.AI cs.LGcited by 32

Not on the map. Why?

Abstract (arXiv)

Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact -- an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick's sensemaking theory, which focuses on who the explanation is intended for. Recent work has advocated for the importance of understanding stakeholders' needs -- we build on this by providing concrete properties (e.g., identity, social context, environmental cues, etc.) that shape human understanding. We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 29 of its 136 references that are in the map. First posted on arXiv 10 May 2022.

Builds on

29 of 136 references.

References · 29

  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. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  5. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  6. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  7. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  8. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  9. 2016The Mythos of Model Interpretabilitycited by 716
  10. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  11. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  12. 2018Manipulating and Measuring Model Interpretabilitycited by 256
17 more
  1. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  2. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  3. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  4. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 115
  5. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  6. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 73
  7. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
  8. 2018Contrastive Explanation: A Structural-Model Approachcited by 64
  9. 2017A causal framework for explaining the predictions of black-box sequence-to-sequence modelscited by 58
  10. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  11. 2018The Challenge of Crafting Intelligible Intelligencecited by 46
  12. 2015Interpretable Classification Models for Recidivism Predictioncited by 44
  13. 2021Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?cited by 37
  14. 2021Towards Relatable Explainable AI with the Perceptual Processcited by 27
  15. 2020Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projectscited by 24
  16. 2021From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidencecited by 17
  17. 2017Simple rules for complex decisionscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 8; 2025: 4; 2026: 7.

32 papers.

Sort:

By area:

  1. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  2. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  3. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  4. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  5. 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
  6. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  7. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  8. 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
  9. 2024Incremental XAI: Memorable Understanding of AI with Incremental Explanationscited by 8
  10. 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
  11. 2024What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blockscited by 6
  12. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agentscited by 6
20 more
  1. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
  2. 2023Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbookcited by 5
  3. 2023Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosiscited by 4
  4. 2024Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakerscited by 3
  5. 2025Evalet: Evaluating Large Language Models through Functional Fragmentationcited by 3
  6. 2024HCC Is All You Need: Alignment-The Sensible Kind Anyway-Is Just Human-Centered Computingcited by 2
  7. 2024DLBacktrace: A Model Agnostic Explainability for any Deep Learning Modelscited by 2
  8. 2025See What I Mean? CUE: A Cognitive Model of Understanding Explanationscited by 2
  9. 2026PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&Acited by 2
  10. 2026Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systemscited by 2
  11. 2023Explaining the ghosts: Feminist intersectional XAI and cartography as methods to account for invisible labourcited by 1
  12. 2023Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Makingcited by 1
  13. 2026Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interactioncited by 1
  14. 2022On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methodscited by 0
  15. 2023A Meta-heuristic Approach to Estimate and Explain Classifier Uncertaintycited by 0
  16. 2024Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosiscited by 0
  17. 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0
  18. 2026Visioning Human-Agentic AI Teaming: Continuity, Tension, and Future Researchcited by 0
  19. 2026NEURON: A Neuro-symbolic System for Grounded Clinical Explainabilitycited by 0
  20. 2026SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketchescited by 0