Neural Avatars and Adaptive Systems: Behavioural Dynamics and Ethical Governance in the Metaverse

Abstract

This research analyzes how artificial intelligence (AI) can be used to further personalize things in the metaverse. To do so, it examines brain models and configurable settings from the perspective of government ethics, human-computer interaction, and behavioral psychology. Biological tracking, psychological testing, behavior monitoring, and qualitative conversations are some of the methods used in the study to demonstrate that making avatars more unique makes people much more interested in the game. It was found that men and women had different tastes. For example, the women who participated valued utility more than artistic reality. The use of operant conditioning to guide adaptive changes led to a 42% increase in dwell time but also raised moral questions. The Integrated Metaverse Engagement Model  (IMEM) under consideration combines information ethics with the idea of social identities. It has design rules aimed at reducing computational bias and making the model compliant with standards such as the General Protection Regulation (GDPR). AI Fairness 360 (AIF360) and Random Forest classification are two of the many methods used in the study. Modern design interfaces such as YOLOv8 with Squeeze-and-Excitation blocks and ConvNeXt-SE-attn models are also used to gain better insight into behavior sequences. The results provide developers and policymakers with evidence-based insights. They also contribute to the academic debate on digital identity and offer moral guidelines that can be used when deploying AI in virtual environments.