# Toward Explainable AI for Regression Models

arXiv: https://arxiv.org/abs/2112.11407 · HTML: https://mlsanity.com/paper/2112.11407/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 91 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 30 of 51)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet](https://mlsanity.com/paper/1904.00760/) (2019)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [Explanations can be manipulated and geometry is to blame](https://mlsanity.com/paper/1906.07983/) (2019)

## Built on by (most cited first, 30 of 31)

- [Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks](https://mlsanity.com/paper/2401.06654/) (2024)
- [Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science](https://mlsanity.com/paper/2303.00652/) (2023)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK](https://mlsanity.com/paper/2304.11218/) (2023)
- [RelP: Faithful and Efficient Circuit Discovery in Language Models via Relevance Patching](https://mlsanity.com/paper/2508.21258/) (2025)
- [Explaining Predictive Uncertainty by Exposing Second-Order Effects](https://mlsanity.com/paper/2401.17441/) (2024)
- [Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks](https://mlsanity.com/paper/2304.05727/) (2023)
- [Towards Desiderata-Driven Design of Visual Counterfactual Explainers](https://mlsanity.com/paper/2506.14698/) (2025)
- [On the Soundness of XAI in Prognostics and Health Management (PHM)](https://mlsanity.com/paper/2303.05517/) (2023)
- [Calibrated Explanations for Regression](https://mlsanity.com/paper/2308.16245/) (2023)
- [Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series](https://mlsanity.com/paper/2203.07861/) (2022)
- [An XAI framework for robust and transparent data-driven wind turbine power curve models](https://mlsanity.com/paper/2304.09835/) (2023)
- [Automatic Identification of Chemical Moieties](https://mlsanity.com/paper/2203.16205/) (2022)
- [eXplainable Artificial Intelligence (XAI) in aging clock models](https://mlsanity.com/paper/2307.13704/) (2023)
- [Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation](https://mlsanity.com/paper/2308.12053/) (2023)
- [Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data](https://mlsanity.com/paper/2410.12803/) (2024)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables](https://mlsanity.com/paper/2211.10444/) (2022)
- [Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework](https://mlsanity.com/paper/2408.08376/) (2024)
- [Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution](https://mlsanity.com/paper/2409.16787/) (2024)
- [xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2411.07643/) (2024)
- [Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanation](https://mlsanity.com/paper/2412.09770/) (2024)
- [A New Perspective On AI Safety Through Control Theory Methodologies](https://mlsanity.com/paper/2506.23703/) (2025)
- [XAI for transparent wind turbine power curve models](https://mlsanity.com/paper/2210.12104/) (2022)
- [Metric Tools for Sensitivity Analysis with Applications to Neural Networks](https://mlsanity.com/paper/2305.02368/) (2023)
- [When a CBR in Hand is Better than Twins in the Bush](https://mlsanity.com/paper/2305.05111/) (2023)
- [XpertAI: uncovering regression model strategies for sub-manifolds](https://mlsanity.com/paper/2403.07486/) (2024)
- [Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning](https://mlsanity.com/paper/2511.20591/) (2025)
- [TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring](https://mlsanity.com/paper/2512.01562/) (2025)
- [Concept-based explanation of gene expression prediction from H&E images](https://mlsanity.com/paper/2608.16669/) (2026)

## Abstract (arXiv)

In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.