# ML Interpretability: Simple Isn't Easy

arXiv: https://arxiv.org/abs/2211.13617 · HTML: https://mlsanity.com/paper/2211.13617/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [The Modern Mathematics of Deep Learning](https://mlsanity.com/paper/2105.04026/) (2021)
- [The no-free-lunch theorems of supervised learning](https://mlsanity.com/paper/2202.04513/) (2022)

## Built on by (most cited first, 7 of 7)

- [Pixel-Grounded Prototypical Part Networks](https://mlsanity.com/paper/2309.14531/) (2023)
- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [This Probably Looks Exactly Like That: An Invertible Prototypical Network](https://mlsanity.com/paper/2407.12200/) (2024)
- [MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification](https://mlsanity.com/paper/2501.02949/) (2025)
- [Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms](https://mlsanity.com/paper/2510.20621/) (2025)
- [Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence](https://mlsanity.com/paper/2507.22197/) (2025)
- [Transparent and Fair Profiling in Employment Services: Evidence from Switzerland](https://mlsanity.com/paper/2509.11847/) (2025)

## Abstract (arXiv)

The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by focussing on the other end of the 'interpretability spectrum'. The reasons why some models, linear models and decision trees, are highly interpretable will be examined, and also how more general models, MARS and GAM, retain some degree of interpretability. I find that while there is heterogeneity in how we gain interpretability, what interpretability is in particular cases can be explicated in a clear manner.