arXiv:2605.20439v1 Announce Type: new Abstract: Explainable AI (XAI) techniques aim to provide insights into predictive models and enhance user performance, yet they often fall short of these expectations. Conversational XAI assistants promise to overcome such limitations, but empirical evidence on their impact on objective performance measures remains limited. We propose an experimental design for evaluating explanation assistance through prediction accuracy, model understanding, and error identification. Using an explainable-by-design prediction model, we create conditions where users can ou
The proliferation of complex AI systems has made the need for interpretability and user trust critical, driving research into practical applications of Explainable AI (XAI). This paper emerges as researchers seek empirical evidence to validate conversational XAI's effectiveness.
Improving the interpretability and utility of AI systems through conversational XAI could significantly enhance user performance and trust, critical for widespread AI adoption across various industries. This study directly addresses a key limitation in current XAI approaches.
This research outlines an experimental design to rigorously evaluate conversational XAI's impact, potentially leading to more effective and trustworthy AI-human collaboration. It shifts the focus from theoretical XAI benefits to measurable user performance gains.
- · AI developers
- · Businesses adopting AI
- · End-users of AI systems
- · Companies with opaque or untrustworthy AI systems
- · Developers neglecting XAI principles
Increased adoption and integration of conversational XAI techniques into commercial AI platforms and products.
Higher user satisfaction and reduced AI-related errors in critical applications due to improved model understanding and error identification.
Enhanced regulatory confidence and easier compliance for AI systems as their interpretability and explainability become empirically verifiable.
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