A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation
This work addresses the need for transparency and trust in social robots for multi-party conversation scenarios, though it appears incremental as it builds on existing methods with added explainability features.
The paper tackles the problem of addressee estimation in multi-party conversations for human-robot interaction by developing a model that improves performance over the state-of-the-art and incorporates explainable attention-based segments, validated in real-time with an iCub robot and a user study to assess human perception.
The addressee estimation (understanding to whom somebody is talking) is a fundamental task for human activity recognition in multi-party conversation scenarios. Specifically, in the field of human-robot interaction, it becomes even more crucial to enable social robots to participate in such interactive contexts. However, it is usually implemented as a binary classification task, restricting the robot's capability to estimate whether it was addressed \review{or not, which} limits its interactive skills. For a social robot to gain the trust of humans, it is also important to manifest a certain level of transparency and explainability. Explainable artificial intelligence thus plays a significant role in the current machine learning applications and models, to provide explanations for their decisions besides excellent performance. In our work, we a) present an addressee estimation model with improved performance in comparison with the previous state-of-the-art; b) further modify this model to include inherently explainable attention-based segments; c) implement the explainable addressee estimation as part of a modular cognitive architecture for multi-party conversation in an iCub robot; d) validate the real-time performance of the explainable model in multi-party human-robot interaction; e) propose several ways to incorporate explainability and transparency in the aforementioned architecture; and f) perform an online user study to analyze the effect of various explanations on how human participants perceive the robot.