Mediators: Conversational Agents Explaining NLP Model Behavior

Conversational explanations are more effective than static ones: The paper argues that framing AI explanations as interactive conversations between humans and machines is more aligned with how people naturally seek understanding, rather than receiving one-time technical explanations.

Current NLP systems lack adequate explanation capabilities: The authors assess existing research and find that we are still far from having conversational agents that can truly explain neural model behavior interactively—highlighting a significant gap between current AI systems and responsible AI needs.

A blueprint for "Mediators" is needed: The paper establishes specific design requirements for conversational explanation agents, proposing that such "Mediators" should be able to answer follow-up questions and adapt explanations based on user needs, similar to how a human expert would explain their reasoning.

Sentiment analysis is a useful testing ground: The authors use sentiment analysis as a concrete case study to demonstrate how conversational explanation systems might work in practice, making the abstract concept of dialogue-based AI explanations more tangible.

Human-centered design is essential for trustworthy AI: By positioning explanation as a conversation rather than a one-way information transfer, the paper emphasizes that responsible AI requires systems designed with human understanding and interaction in mind, not just technical accuracy.