Abstracted Trajectory Visualization for Explainability in Reinforcement Learning

Non-experts need better tools to understand AI decision-making: The paper identifies a significant gap in explainable AI research—while XAI methods exist for RL specialists, non-technical stakeholders lack adequate tools to understand how RL models work, limiting their ability to participate in important discussions about AI design and deployment.

Visualization of simplified agent behavior can bridge the expertise gap: The research demonstrates that "abstracted trajectories"—visual representations showing transitions between major states rather than all technical details—help non-RL experts understand agent behavior patterns without requiring specialized knowledge.

Democratizing AI understanding is essential for responsible AI development: By enabling non-experts to grasp how RL models function, this approach supports more inclusive decision-making about AI systems, allowing diverse stakeholders to contribute to designing AI that "better fits our society" rather than leaving these choices solely to technical experts.

Mental models matter for meaningful AI oversight: The paper suggests that helping non-experts build accurate "mental models of agents" through accessible visualization is a practical step toward enabling meaningful human oversight and participation in AI governance discussions.