Toward Explainable AI for Regression Models

Abstract

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.

17 pages, 10 figures, published; changes: 1. references to code and xai-regression.org added (p. 1/2, end of introduction), 2. adjustment of sign-error in restructuring section (p. 8, just above Fig. 4)

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Methods for Interpreting and Understanding Deep Neural Networks
• Striving for Simplicity: The All Convolutional Net
• Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• Machine learning for molecular simulation
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
• Explaining and Interpreting LSTMs
• Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
• Counterfactual Explanations for Arbitrary Regression Models

Cited by in corpus (7)

• Neural Network Potentials for Chemistry: Concepts, Applications and Prospects
• A Survey and Comparison of Post-quantum and Quantum Blockchains
• An XAI framework for robust and transparent data-driven wind turbine power curve models
• PredDiff: Explanations and Interactions from Conditional Expectations
• Calibrated Explanations for Regression
• Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
• Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework