The Emerging Landscape of Explainable AI Planning and Decision Making

Explanations in AI planning are increasingly designed for specific users rather than generic audiences. The paper contrasts earlier approaches with newer work that tailors explanations to different stakeholder needs—such as domain experts versus end-users—recognizing that one-size-fits-all explanations are less effective for building trust and understanding in automated decision-making systems.

Human-in-the-loop systems require explanations as a core design feature, not an afterthought. The survey emphasizes that for AI planning systems to work effectively with human oversight, explanations must be integrated from the beginning of system design rather than added later, which is critical for responsible deployment of autonomous decision-making.

The field has evolved from purely technical explanations to multiple delivery mechanisms. The paper documents how explainable AI planning has moved beyond just providing reasoning traces to using diverse formats and channels for communicating how and why AI systems make decisions, making explanations more accessible to non-technical stakeholders.

There is growing recognition that different planning contexts require different explanation approaches. The emerging landscape shows researchers developing specialized explanation techniques for various domains and decision types, suggesting that responsible AI requires context-aware rather than universal explanation strategies.