From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent

Conversational explanations are more effective than static ones: The paper emphasizes that social science research shows explanations should mimic human-to-human conversations rather than presenting information in traditional formats, suggesting that interactive dialogue is a more natural and potentially more effective way for people to understand AI systems.

There's a gap between what AI systems can explain and what users want to know: The researchers developed an "XAI question bank" with quality-controlled paraphrases to capture the diverse ways people ask about AI reasoning, indicating that users have varied information needs that go beyond what most current explanation systems provide.

Multiple explanation methods exist but need systematic matching to user questions: The paper presents a comprehensive survey of explanation methods matched to specific user questions, suggesting that responsible AI requires not just having explanation capabilities, but deliberately connecting the right explanation technique to what users actually want to understand about a model's decisions.

Conversational AI agents can be designed to explain "black box" models: By integrating natural language understanding and generation components with XAI methods, the researchers demonstrate that it's technically feasible to create systems that can have genuine conversations about how complex AI models work, making AI transparency more accessible to non-technical users.

Open resources can accelerate responsible AI development: The authors released their source code, data, and comprehensive list of XAI questions and methods, recognizing that shared resources are necessary for other researchers and developers to build more transparent and explainable AI systems.