AI Ethics and Bias in Machine Learning Models in AR and VR: a Focus on Education, Healthcare and Finance

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Augmented Reality (AR) and Virtual Reality (VR) environments is transforming how users experience education, healthcare, and digital engagement. However, the combination of immersive interfaces with AI-driven personalization introduces heightened risks of algorithmic bias, user discrimination, and ethical oversight gaps. In spatially responsive and sensor-rich environments, even subtle biases in training data or system design can result in unequal treatmentsuch as misrepresented avatars, exclusionary learning experiences, or inequitable diagnostic insights. This paper explores the ethical dimensions of deploying AI systems within AR/VR technologies, focusing on high-impact domains like educational simulations, clinical training, and immersive digital services. Drawing on recent frameworks such as XRAI-Ethics and EVRIM, we review the sources and consequences of bias in extended AI systems and propose best practices to detect, mitigate, and monitor them across the development lifecycle. Our analysis synthesizes technical strategies, inclusive design principles, and policy recommendations to advance fairness, transparency, and accountability in immersive AI applications. Provided these findings, we propose a high-level framework that should provide a roadmap for stakeholders seeking to build responsible, equitable, and human-centered AI systems within emerging virtual platforms.