What is the concept of explainable AI in robotics, and how do researchers and developers ensure transparency, accountability, and trustworthiness in robotic systems equipped with artificial intelligence algorithms for decision-making, perception, and learning?

Explainable AI involves designing and developing robotic systems with interpretable and transparent decision-making processes that enable users to understand, validate, and trust the system’s behavior. Researchers employ techniques such as model explainability, algorithmic transparency, and human-robot interaction design to provide insights into AI-based reasoning, predictions, and actions, fostering accountability and user acceptance in safety-critical applications such as autonomous vehicles, healthcare robotics, and human-robot collaboration scenarios.