Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework

Autonomous robots need better accountability systems: Current deep learning-powered robots cannot explain their decisions when accidents happen, creating a serious gap in responsibility. TRACE addresses this by documenting the reasoning chain from sensor data to final action.

The framework achieves high auditability rates: In testing, TRACE demonstrated 98.6% evidence traceability (linking decisions to sensor data), 99.0% temporal continuity (complete audit trails), and 98.1% decision reconstructability (ability to understand past decisions after the fact).

Explainability tools alone aren't enough: While post-hoc methods like LIME can identify which features influenced a decision, they don't provide the structured documentation needed to fully reconstruct why a robot made a specific choice—TRACE's layered approach fills this gap.

The system works with existing AI technology: TRACE is designed to work alongside current learning-based perception systems (like CNNs and transformers) rather than replacing them, making it practical for real-world deployment in autonomous robots.

This framework helps meet regulatory requirements: By creating transparent decision-making processes with documented causal chains, TRACE helps autonomous systems comply with emerging regulations like the EU AI Act that require accountability and auditability.