# The Doctor Just Won't Accept That!

arXiv: https://arxiv.org/abs/1711.08037 · HTML: https://mlsanity.com/paper/1711.08037/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 21 of 21)

- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations](https://mlsanity.com/paper/2101.08758/) (2021)
- [Explainable Deep Learning Methods in Medical Image Classification: A Survey](https://mlsanity.com/paper/2205.04766/) (2022)
- [Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis](https://mlsanity.com/paper/2304.04579/) (2023)
- [XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization](https://mlsanity.com/paper/2403.09410/) (2024)
- [MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment](https://mlsanity.com/paper/2401.08527/) (2024)
- [Interpretable Mixture of Experts](https://mlsanity.com/paper/2206.02107/) (2022)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient](https://mlsanity.com/paper/2202.05302/) (2022)
- [VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models](https://mlsanity.com/paper/2108.02550/) (2021)
- [PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs](https://mlsanity.com/paper/2108.13828/) (2021)
- [AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis](https://mlsanity.com/paper/2408.02001/) (2024)
- [How explainable are adversarially-robust CNNs?](https://mlsanity.com/paper/2205.13042/) (2022)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [A Framework for Interpretability in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2310.01685/) (2023)
- [Transcending XAI Algorithm Boundaries through End-User-Inspired Design](https://mlsanity.com/paper/2208.08739/) (2022)
- [From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection](https://mlsanity.com/paper/2505.06003/) (2025)
- [Explainable Supervised Domain Adaptation](https://mlsanity.com/paper/2205.09943/) (2022)
- [ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis](https://mlsanity.com/paper/2411.01564/) (2024)
- [Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression](https://mlsanity.com/paper/2504.05756/) (2025)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)

## 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.