# Do Not Trust Additive Explanations

arXiv: https://arxiv.org/abs/1903.11420 · HTML: https://mlsanity.com/paper/1903.11420/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [PredDiff: Explanations and Interactions from Conditional Expectations](https://mlsanity.com/paper/2102.13519/) (2021)
- [Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations](https://mlsanity.com/paper/2101.09498/) (2021)
- [Why do explanations fail? A typology and discussion on failures in XAI](https://mlsanity.com/paper/2405.13474/) (2024)
- [X Hacking: The Threat of Misguided AutoML](https://mlsanity.com/paper/2401.08513/) (2024)
- [When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances](https://mlsanity.com/paper/2406.14349/) (2024)
- [shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python](https://mlsanity.com/paper/2504.01842/) (2025)
- [Trustworthy AI Must Account for Interactions](https://mlsanity.com/paper/2504.07170/) (2025)
- [Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models](https://mlsanity.com/paper/2111.04658/) (2021)
- [ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification](https://mlsanity.com/paper/2509.14830/) (2025)
- [Neuro-Symbolic Rule Lists](https://mlsanity.com/paper/2411.06428/) (2024)
- [Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making](https://mlsanity.com/paper/2604.14240/) (2026)
- [Causality-Aware Local Interpretable Model-Agnostic Explanations](https://mlsanity.com/paper/2212.05256/) (2022)
- [Succinct Interaction-Aware Explanations](https://mlsanity.com/paper/2402.05566/) (2024)
- [Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy](https://mlsanity.com/paper/2602.22288/) (2026)
- [Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models](https://mlsanity.com/paper/2606.10347/) (2026)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise instance-level interpretability for any complex machine learning model. But how faithful are these additive explanations? Can we rely on additive explanations for non-additive models? In this paper, we (1) examine the behavior of the most popular instance-level explanations under the presence of interactions, (2) introduce a new method that detects interactions for instance-level explanations, (3) perform a large scale benchmark to see how frequently additive explanations may be misleading.