BaTCAVe: Trustworthy Explanations for Robot Behaviors
This addresses the need for interpretable AI in robotics for stakeholders like engineers and legislative bodies, though it appears incremental as it builds on existing explainable AI methods.
The paper tackles the problem of explaining robot behaviors by introducing a technique that provides trustworthy explanations with uncertainty scores, validated through experiments on simulated and real-world robot models.
Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and legislative bodies, lack insights into the neural networks' decision-making process. Presently, explainable AI is primarily tailored to natural language processing and computer vision, falling short in two critical aspects when applied in robots: grounding in decision-making tasks and the ability to assess trustworthiness of their explanations. In this paper, we introduce a trustworthy explainable robotics technique based on human-interpretable, high-level concepts that attribute to the decisions made by the neural network. Our proposed technique provides explanations with associated uncertainty scores for the explanation by matching neural network's activations with human-interpretable visualizations. To validate our approach, we conducted a series of experiments with various simulated and real-world robot decision-making models, demonstrating the effectiveness of the proposed approach as a post-hoc, human-friendly robot diagnostic tool.