mlsanityData to 7 Oct 2026

Interpretable Machine Learning: Moving From Mythos to Diagnostics

arXiv 2103.06254 ↗First posted 10 Mar 2021cs.LGcited by 9

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Abstract (arXiv)

Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases. In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workflow to better enable researchers and consumers to work together to discover what types of methods are useful for what use cases. Eventually, by building on the results generated from this workflow, a more complete version of the taxonomy will increasingly allow consumers to find relevant methods for their target use cases and researchers to identify applicable use cases for their proposed methods.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

32 of 53 references.

References · 32

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  5. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  6. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  7. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  8. 2018Sanity Checks for Saliency Mapscited by 1,149
  9. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  10. 2016The Mythos of Model Interpretabilitycited by 716
  11. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  12. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 467
20 more
  1. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  2. 2018Actionable Recourse in Linear Classificationcited by 327
  3. 2018On the Robustness of Interpretability Methodscited by 310
  4. 2019Interpretable machine learning: definitions, methods, and applicationscited by 303
  5. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  6. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 232
  7. 2017Distilling a Neural Network Into a Soft Decision Treecited by 230
  8. 2019Explainable Machine Learning in Deploymentcited by 210
  9. 2018Representer Point Selection for Explaining Deep Neural Networkscited by 187
  10. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  11. 2019Learning Model-Agnostic Counterfactual Explanations for Tabular Datacited by 146
  12. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  13. 2018Model Agnostic Supervised Local Explanationscited by 83
  14. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 73
  15. 2020Efficient nonparametric statistical inference on population feature importance using Shapley valuescited by 61
  16. 2014Falling Rule Listscited by 58
  17. 2019Issues with post-hoc counterfactual explanations: a discussioncited by 30
  18. 2018Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Correctionscited by 19
  19. 2020Explaining Groups of Points in Low-Dimensional Representationscited by 16
  20. 2020A Learning Theoretic Perspective on Local Explainabilitycited by 11

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 3.

9 papers.

  1. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  2. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  3. 2022DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanationscited by 34
  4. 2023Selective Explanations: Leveraging Human Input to Align Explainable AIcited by 29
  5. 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
  6. 2024Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaborationcited by 8
  7. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  8. 2024Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelinescited by 2
  9. 2024Foundations of Multisensory Artificial Intelligencecited by 0