Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategies

Superhuman AI strategies are inherently difficult to explain: Deep Reinforcement Learning systems can develop solutions that outperform humans in complex strategic problems, but these strategies are "non-intuitive and complex by definition," making them fundamentally challenging to translate into human-understandable explanations that domain experts need.

Current explainability tools fall short for strategic AI: While Explainable AI (XAI) has improved transparency in many AI applications, existing approaches have not yet provided the domain-level insights that expert users require when deploying these systems in real-world strategic situations, creating a critical gap in trustworthiness.

Real-world deployment creates unique trust barriers: The paper identifies that real-world scenarios "prohibit reliable performance evaluation," meaning we cannot easily verify whether superhuman AI strategies will work as expected outside controlled environments—a fundamental obstacle to building justified trust in these systems.

Trust requires more than just performance metrics: The authors argue that transparency and reliability are "key components for trust" that go beyond simply knowing an AI system performs well; stakeholders need to understand why and how the system makes strategic decisions at a level meaningful to domain experts.

Explainability is essential infrastructure for responsible deployment: The paper frames explainability as "a key technology" for responsible AI, suggesting that without solving the explanation problem for superhuman strategies, deploying these systems in high-stakes real-world applications carries "significant risks."