DP
D.V. Pandeva
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Influence of Global Explanations on Human Supervision and Trust in Agent
Explainable AI for human supervision over firefighting robots
With the rise of AI presence in various contexts and spheres of life, ensuring effective human-AI collaboration, especially in critical domains, is of utmost importance. Explanations given by AI agent can be of great assistance for this purpose. This study investigates the impact of global explanations, explaining general allocation rules, on human supervision and trust in AI within the critical domain of a firefighting scenario, where human and AI agent have to collaborate to save victims. To this end, a user study involving 40 participants was performed. The user study compared the baseline and global explanation scenario and the participants’ trust in the AI and explanation satisfaction were measured. The results indicated no significant differences between the two types of explanations, and in fact both achieved similar satisfactory outcomes. This suggests comparable effectiveness of global explanations in enhancing human-AI collaboration. Essentially, the insights of this study underscore the need for further exploration into contextual factors influencing the impact of global explanations and contribute to designing better human-AI teaming systems in dynamic and ethically sensitive environments.
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With the rise of AI presence in various contexts and spheres of life, ensuring effective human-AI collaboration, especially in critical domains, is of utmost importance. Explanations given by AI agent can be of great assistance for this purpose. This study investigates the impact of global explanations, explaining general allocation rules, on human supervision and trust in AI within the critical domain of a firefighting scenario, where human and AI agent have to collaborate to save victims. To this end, a user study involving 40 participants was performed. The user study compared the baseline and global explanation scenario and the participants’ trust in the AI and explanation satisfaction were measured. The results indicated no significant differences between the two types of explanations, and in fact both achieved similar satisfactory outcomes. This suggests comparable effectiveness of global explanations in enhancing human-AI collaboration. Essentially, the insights of this study underscore the need for further exploration into contextual factors influencing the impact of global explanations and contribute to designing better human-AI teaming systems in dynamic and ethically sensitive environments.