Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

Understanding AI explanations is just the first step: The paper emphasizes that simply knowing why an AI model makes decisions (traditional explainable AI) is insufficient; the field is moving toward actively using these explanations to improve how models actually behave and make decisions.

Explanations can be used to guide AI toward fairness and accountability: Explanation-Guided Learning techniques allow engineers to steer AI models toward important societal values like fairness, accountability, and transparency by adding constraints based on model explanations—moving beyond just accuracy metrics to include ethical considerations.

Multiple methods exist to incorporate explanations into model improvement: The survey identifies various approaches including adding regularization (penalties for unwanted patterns), additional supervision (teaching the model what explanations should look like), and direct intervention on how models explain themselves.

Evaluation of explanation-guided improvements remains complex: The paper highlights that assessing whether these techniques actually work requires multiple evaluation factors and metrics beyond traditional accuracy measures, suggesting the field still lacks standardized ways to verify that AI systems are genuinely becoming more trustworthy and aligned with human values.

This approach has broad real-world potential: The techniques surveyed could be applied across many domains where responsible AI matters—from healthcare to criminal justice—by ensuring AI systems not only perform well but also reason in ways that align with human expectations and ethical principles.