# Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

arXiv: https://arxiv.org/abs/1811.10154 · HTML: https://mlsanity.com/paper/1811.10154/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 30 of 40)

- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [ProtGNN: Towards Self-Explaining Graph Neural Networks](https://mlsanity.com/paper/2112.00911/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Augmenting Interpretable Models with LLMs during Training](https://mlsanity.com/paper/2209.11799/) (2022)
- [Local Interpretations for Explainable Natural Language Processing: A Survey](https://mlsanity.com/paper/2103.11072/) (2021)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [Foundations of Symbolic Languages for Model Interpretability](https://mlsanity.com/paper/2110.02376/) (2021)
- [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://mlsanity.com/paper/2407.19200/) (2024)
- [Bayesian Concept Bottleneck Models with LLM Priors](https://mlsanity.com/paper/2410.15555/) (2024)
- [CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis](https://mlsanity.com/paper/2301.01642/) (2023)
- [Diagnosing AI Explanation Methods with Folk Concepts of Behavior](https://mlsanity.com/paper/2201.11239/) (2022)
- [Fast Interpretable Greedy-Tree Sums](https://mlsanity.com/paper/2201.11931/) (2022)
- [Simulating Society Requires Simulating Thought](https://mlsanity.com/paper/2506.06958/) (2025)
- [Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction](https://mlsanity.com/paper/2401.01549/) (2024)
- [Interpretation of Black Box NLP Models: A Survey](https://mlsanity.com/paper/2203.17081/) (2022)
- [Provable concept learning for interpretable predictions using variational autoencoders](https://mlsanity.com/paper/2204.00492/) (2022)
- [Developing a Fidelity Evaluation Approach for Interpretable Machine Learning](https://mlsanity.com/paper/2106.08492/) (2021)
- [Automated Learning of Interpretable Models with Quantified Uncertainty](https://mlsanity.com/paper/2205.01626/) (2022)
- [Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning](https://mlsanity.com/paper/2102.12328/) (2021)
- [Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions](https://mlsanity.com/paper/2209.15409/) (2022)
- [Learning a Decision Tree Algorithm with Transformers](https://mlsanity.com/paper/2402.03774/) (2024)
- [Less is More: The Influence of Pruning on the Explainability of CNNs](https://mlsanity.com/paper/2302.08878/) (2023)
- [The Blame Problem in Evaluating Local Explanations, and How to Tackle it](https://mlsanity.com/paper/2310.03466/) (2023)
- ["Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI](https://mlsanity.com/paper/2207.00007/) (2022)
- [Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning](https://mlsanity.com/paper/2405.15789/) (2024)
- [Towards Few-shot Self-explaining Graph Neural Networks](https://mlsanity.com/paper/2408.07340/) (2024)
- [Learning accurate and interpretable tree-based models](https://mlsanity.com/paper/2405.15911/) (2024)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners](https://mlsanity.com/paper/2202.01153/) (2022)
- [Topological Interpretability for Deep-Learning](https://mlsanity.com/paper/2305.08642/) (2023)

## Abstract (arXiv)

Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to textitexplain black box models, rather than creating models that are textitinterpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.