INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations

One-size-fits-all explanations are insufficient: Current AI explanation systems provide only a single explanation for their decisions, but this fails to recognize that different people understand and evaluate AI reasoning differently based on their backgrounds and experiences. The INTERACTION framework addresses this by generating multiple diverse explanations for the same decision.

Diverse explanations improve transparency: The paper demonstrates that a generative approach can produce varied evidence for AI predictions while maintaining or improving accuracy (up to 4.4% gain) and explanation quality (up to 4.7% improvement in BLEU scores). This suggests that accommodating human diversity doesn't require sacrificing AI performance.

Human-centered design matters for XAI: By incorporating principles from human-computer interaction (HCI), the research shows that explainability systems should be designed with the understanding that users have diverse needs and perspectives, rather than assuming a universal explanation will serve everyone equally well.

Natural language explanations require sophisticated methods: The use of conditional variational autoencoders enables the system to generate contextually appropriate, multiple explanations rather than simple, repetitive ones—suggesting that truly useful AI transparency requires advanced technical approaches tailored to language's complexity.