# Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis

arXiv: https://arxiv.org/abs/2312.13234 · HTML: https://mlsanity.com/paper/2312.13234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 113 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Towards better understanding of gradient-based attribution methods for Deep Neural Networks](https://mlsanity.com/paper/1711.06104/) (2017)
- [Algorithm-Agnostic Explainability for Unsupervised Clustering](https://mlsanity.com/paper/2105.08053/) (2021)

## Abstract (arXiv)

We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics. We address both researchers and practitioners, calling attention to the benefits of a unified SA-based view of explanations in ML and the necessity to fully credit related work. We bridge the gap between both fields by formally describing how (a) the ML process is a system suitable for SA, (b) how existing ML interpretation methods relate to this perspective, and (c) how other SA techniques could be applied to ML.