ABSTRACT Explainable AI (XAI) is essential for building trust in Deep Neural Networks (DNNs). SHAP (SHapley Additive exPlanations) is a well‐known XAI technique for attributing feature importance, but it struggles with exponential computational complexity as the number of features increases. Various approximation methods have been suggested, but they compromise SHAP's theoretical principles. We introduce AA‐SHAP, a novel approach that derives superpixel affinity from the explained model's internals to identify and group superpixels. AA‐SHAP constructs a relevance‐consistency affinity between superpixel interdependence, enabling much faster SHAP calculations on a reduced set of meta‐superpixels while outperforming previous methods in explanation faithfulness. Exact Shapley values are computed on the reduced meta‐superpixel game, preserving all axiomatic guarantees within the aggregated feature space. Evaluated across multiple datasets, both convolutional and transformer classification architectures show that AA‐SHAP produces more faithful attributions than competing methods while improving computational speed and maintaining SHAP's theoretical axioms. The source code is available at https://github.com/vhasic/AA‐SHAP .

As the use of autonomous mobile robots expands into dynamic and complex environments, the need for them to provide understandable explanations for their actions becomes crucial. This thesis addresses the challenge of developing explainability for robot navigation by leveraging a hybrid model that combines machine learning techniques with symbolic reasoning methods. Furthermore, the thesis explores the modeling of human explanation preferences and the impact of different explanation attributes on explanation recipients' understanding, satisfaction, and trust. The goal is to integrate different explanation aspects and approaches into a unified framework to support explainable navigation in robotics.

This paper introduces affordance-based explanations of robot navigational decisions. The rationale behind affordance-based explanations draws on the theory of affordances, a principle rooted in ecological psychology that describes potential actions the objects in the environment offer to the robot. We demonstrate how affordances can be incorporated into visual and textual explanations for common robot navigation and path-planning scenarios. Furthermore, we formalize and categorize the concept of affordance-based explanations and connect it to existing explanation types in robotics. We present the results of a user study that shows participants to be, on average, highly satisfied with visual-textual, i.e., multimodal, affordance-based explanations of robot navigation. Furthermore, we investigate the complexity of different types of textual affordance-based explanations. Our research contributes to the expanding domain of explainable robotics, focusing on explaining robot actions in navigation.

The choices made by autonomous robots in social settings bear consequences for humans and their presumptions of robot behavior. Explanations can serve to alleviate detrimental impacts on humans and amplify their comprehension of robot decisions. We model the process of explanation generation for robot navigation as an automated planning problem considering different possible explanation attributes. Our visual and textual explanations of a robot’s navigation are influenced by the robot’s personality. Moreover, they account for different contextual, environmental, and spatial characteristics. We present the results of a user study demonstrating that users are more satisfied with multimodal than unimodal explanations. Additionally, our findings reveal low user satisfaction with explanations of a robot with extreme personality traits. In conclusion, we deliberate on potential future research directions and the associated constraints. Our work advocates for fostering socially adept and safe autonomous robot navigation.