Domain-Level Explainability -- A Challenge for Creating Trust in
  Superhuman AI Strategies
  For strategic problems, intelligent systems based on Deep Reinforcement
Learning (DRL) have demonstrated an impressive ability to learn advanced
solutions that can go far beyond human capabilities, especially when dealing
with complex scenarios. While this creates new opportunities for the
development of intelligent assistance systems with groundbreaking
functionalities, applying this technology to real-world problems carries
significant risks and therefore requires trust in their transparency and
reliability. With superhuman strategies being non-intuitive and complex by
definition and real-world scenarios prohibiting a reliable performance
evaluation, the key components for trust in these systems are difficult to
achieve. Explainable AI (XAI) has successfully increased transparency for
modern AI systems through a variety of measures, however, XAI research has not
yet provided approaches enabling domain level insights for expert users in
strategic situations. In this paper, we discuss the existence of superhuman
DRL-based strategies, their properties, the requirements and challenges for
transforming them into real-world environments, and the implications for trust
through explainability as a key technology.
