mlsanityData to 7 Oct 2026

The Doctor Just Won't Accept That!

arXiv 1711.08037 ↗First posted 20 Nov 2017stat.MLcited by 21

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Abstract (arXiv)

Calls to arms to build interpretable models express a well-founded discomfort with machine learning. Should a software agent that does not even know what a loan is decide who qualifies for one? Indeed, we ought to be cautious about injecting machine learning (or anything else, for that matter) into applications where there may be a significant risk of causing social harm. However, claims that stakeholders "just won't accept that!" do not provide a sufficient foundation for a proposed field of study. For the field of interpretable machine learning to advance, we must ask the following questions: What precisely won't various stakeholders accept? What do they want? Are these desiderata reasonable? Are they feasible? In order to answer these questions, we'll have to give real-world problems and their respective stakeholders greater consideration.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Nov 2017.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 3; 2024: 4; 2025: 2.

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  1. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  2. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  3. 2022Explainable Deep Learning Methods in Medical Image Classification: A Surveycited by 31
  4. 2023Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosiscited by 26
  5. 2024XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimizationcited by 20
  6. 2024MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignmentcited by 18
  7. 2022Interpretable Mixture of Expertscited by 17
  8. 2021FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imagingcited by 13
  9. 2022Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficientcited by 13
  10. 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
  11. 2021PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNscited by 12
  12. 2024AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosiscited by 12
9 more
  1. 2022How explainable are adversarially-robust CNNs?cited by 9
  2. 2023Meta-learning in healthcare: A surveycited by 9
  3. 2023A Framework for Interpretability in Machine Learning for Medical Imagingcited by 9
  4. 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Designcited by 2
  5. 2025From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selectioncited by 2
  6. 2022Explainable Supervised Domain Adaptationcited by 1
  7. 2024ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosiscited by 1
  8. 2025Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regressioncited by 1
  9. 2022Introduction to Machine Learning for Physicians: A Survival Guide for Data Delugecited by 0