Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace

TL;DR

This paper discusses the importance of developing user-centered trustworthiness metrics for AI in immersive cyberspace, highlighting challenges and proposing a research agenda for future work.

Contribution

It provides an overview of fairness, privacy, and robustness in trustworthy AI and proposes a research agenda tailored for immersive ecosystems like the metaverse.

Findings

Existing TAI metrics are insufficient for immersive environments.

Challenges include system performance and user experience assessment.

A research agenda for user-centered trustworthy AI is proposed.

Abstract

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.