Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

XAI tools are underutilized: While many methods exist to explain AI model decisions, they are "seldomly used beyond visualization purposes," meaning most organizations are not yet leveraging explanations to actually improve their AI systems in practice.

Explanations can improve multiple model properties: The research demonstrates that XAI-based approaches can enhance various aspects of ML models beyond just accuracy, including generalization ability and reasoning quality, suggesting broader benefits than traditionally measured.

Success depends on context: The effectiveness of using explanations to improve models "can vary depending on a multitude of factors, such as the model and dataset used, or the employed explanation method," indicating that there is no one-size-fits-all approach to responsible AI improvement.

Careful application is essential: While XAI-based model improvement "can have significant beneficial effects," the authors caution that these methods "need to be applied carefully," warning against assuming explanations will automatically lead to better outcomes without proper consideration of implementation details.

The field is still maturing: This paper represents an emerging effort to move XAI from a transparency tool to a practical improvement mechanism, suggesting that organizations adopting these techniques are still in early stages of understanding best practices for responsible AI development.