Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
This work is incremental, aiming to improve AI trustworthiness for users in immersive cyberspace by refining metrics.
The paper addresses the challenge of developing trustworthy AI metrics for immersive ecosystems like the metaverse, where existing approaches are inadequate due to system complexity and user experience assessment, and proposes a research agenda for user-centered solutions.
AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences. Thus, the trustworthiness of such AI systems is vital as failures in these systems can cause serious user harm. Although there are related works on exploring trustworthy AI (TAI) metrics in the current cyberspace, ecosystems towards user-centered services, such as the metaverse, are much more complicated in terms of system performance and user experience assessment, thus posing challenges for the applicability of existing approaches. Thus, we give an overlook on fairness, privacy and robustness, across the historical path from existing approaches. Eventually, we propose a research agenda towards systematic yet user-centered TAI in immersive ecosystems.