Enhancing User Trust in Autonomous Vehicles through Explainable AI-A Human Computer Interaction Perspective: Enhances user trust in AVs through explainable AI systems from a human-computer interaction perspective
Keywords:
Human Factors, User TrustAbstract
The emergence of autonomous vehicles (AVs) presents a transformative shift in personal transportation. However, widespread adoption hinges on user trust in the safety and reliability of these complex systems. This research paper investigates the role of Explainable Artificial Intelligence (XAI) in enhancing user trust in AVs, specifically from a Human-Computer Interaction (HCI) perspective.
The paper begins by outlining the inherent challenges to user trust in AVs. Unlike human drivers, AVs rely on opaque AI algorithms to navigate the environment. This lack of transparency can lead to anxiety and a sense of relinquishing control. Additionally, the potential for unforeseen situations and system errors can further erode user confidence.
The paper then explores the potential of XAI to bridge this trust gap. XAI techniques aim to make the decision-making processes of AI systems more comprehensible to humans. By providing explanations for an AV's actions, users can gain insights into the system's reasoning and rationale behind maneuvers. This transparency can foster a sense of trust and predictability in the user experience.
The core of the paper delves into HCI considerations for implementing XAI in AVs. It emphasizes that effective XAI design goes beyond simply presenting raw data. The explanations need to be tailored to the user's needs, knowledge level, and situation. The paper explores various HCI principles for XAI design in AVs.
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