[Paper Review] Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategies

Jonas Andrulis, Ole Meyer+3 more|arXiv (Cornell University)|Nov 12, 2020

Explainable Artificial Intelligence (XAI)45 references4 citations

TL;DR

This paper argues that domain-level explainability is essential for building trust in superhuman AI strategies derived from Deep Reinforcement Learning (DRL), especially in complex, real-world strategic environments. It proposes that current XAI methods fail to provide meaningful, expert-understandable explanations of counterintuitive DRL policies, and calls for new explainability frameworks rooted in strategic reasoning, scenario planning, and uncertainty modeling to enable human experts to interpret and trust superhuman AI decisions.

ABSTRACT

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.

Motivation & Objective

• To identify the core challenges in explaining superhuman DRL strategies in complex, real-world strategic environments.
• To argue that current XAI methods are insufficient for providing domain-level transparency to non-technical experts.
• To propose that strategic explainability—focused on future projections, hypotheticals, risk, and uncertainty—must be integrated into DRL systems to enable trust.
• To bridge the gap between technical AI explainability and domain-specific strategic understanding for expert users.
• To advocate for the adaptation of established strategy modeling tools (e.g., scenario planning) into XAI frameworks for DRL agents.

Proposed method

• To analyze the strategic complexity of DRL agents through seven core challenges (C1–C7), including spatial-temporal reasoning, collaboration, uncertainty, resource management, opponent modeling, adversarial planning, and large state/action spaces.
• To map these challenges to four key dimensions of strategic explainability (E1–E4): future projections, hypothetical scenarios, risk and safety, and model uncertainty.
• To propose that domain-level explainability must go beyond input-output explanations and instead model strategic reasoning in ways accessible to non-technical domain experts.
• To draw on scenario planning methodologies as a foundation for designing explainable AI systems that simulate future states and 'what-if' outcomes.
• To emphasize the need for XAI systems that quantify uncertainty and detect out-of-distribution cases to improve transparency and safety.
• To advocate for the integration of explainability tools directly into AI agents, making them readily available for expert validation and trust-building.

Experimental results

Research questions

• RQ1 How can explainability in DRL systems be enhanced to support domain experts in understanding counterintuitive, superhuman strategies in complex strategic environments?
• RQ2 What are the key structural and cognitive barriers that prevent current XAI methods from delivering meaningful, domain-level explanations for DRL-based strategic decisions?
• RQ3 In what ways can established strategy modeling techniques—such as scenario planning—be adapted to provide explainability for DRL agents in real-world applications?
• RQ4 How can explainability systems address uncertainty, risk, and hypothetical future states in a way that is both quantitatively sound and intuitively interpretable for non-expert users?
• RQ5 What are the necessary conditions for trust in superhuman AI strategies when empirical validation is infeasible, and how can explainability fulfill this role?

Key findings

• Current XAI methods, including post-hoc explanation models, fail to preserve the strategic complexity of superhuman DRL policies, especially when simplified for interpretability.
• Superhuman DRL strategies are inherently non-intuitive and cannot be meaningfully explained using standard input-output or contrastive explanation techniques alone.
• Domain-level explainability must address seven core strategic challenges: spatial-temporal reasoning, collaboration, decision-making under uncertainty, resource management, opponent modeling, adversarial planning, and large state/action spaces.
• The most promising path forward involves adapting scenario planning tools to DRL explainability, enabling future projections (E1), hypothetical scenario analysis (E2), risk quantification (E3), and uncertainty modeling (E4).
• There is a critical need for explainability frameworks that are context-aware, user-adaptive, and capable of representing long-term strategic reasoning beyond immediate actions.
• Without domain-level strategic explainability, trust in superhuman DRL systems cannot be established, even if they outperform human experts in complex real-world scenarios.