Towards Explainable Strategy Templates using NLP Transformers

AI strategies can be translated into plain English: The paper demonstrates that complex negotiation strategies learned by AI agents through deep reinforcement learning can be converted into human-readable explanations using natural language processing, making AI decision-making more transparent to non-experts.

Multiple steps are needed for meaningful explanations: The approach requires parsing mathematical expressions, interpreting variables, generating initial rule-based explanations, and then refining them with large language models—suggesting that explainability requires deliberate, multi-stage effort rather than occurring automatically.

Explanations can be customized for different audiences: The paper notes that explanations undergo "customization for varied audiences," indicating that the same AI strategy may need different levels of detail or framing depending on whether it's being explained to business stakeholders, regulators, or other groups.

Validation of explanations is critical: The authors emphasize "meticulous validation processes," highlighting that simply generating natural language descriptions of AI behavior isn't sufficient—these explanations must be carefully checked to ensure they accurately represent what the AI is actually doing.

This bridges a real gap in AI transparency: By connecting mathematical AI strategies to comprehensible narratives, this work addresses a practical barrier to responsible AI deployment: the difficulty of explaining automated decision-making to stakeholders who need to understand and trust these systems.