Pin Lean is a panelist in the section titled Content and Conduct Moderation in the Metaverse. Her presentation is titled ‘Safeguarding Virtual Spaces for Children: Addressing Content Policing and Moderation in the Metaverse to Combat Grooming and Sexual Assault’. As the Metaverse emerges as a ubiquitous virtual environment, offering unprecedented opportunities for social interaction, creativity, and commerce, it also presents novel challenges in ensuring the safety and well-being of its users, especially children. Alarming trends have indicated the concerning phenomena that minors are being groomed in these virtual spaces, where sexual predators exploit virtual spaces to manipulate and exploit vulnerable individuals, often under the guise of friendship or mentorship.

Despite growing awareness of these issues, the policing of content and moderation within the Metaverse remains woefully inadequate. Existing mechanisms often fall short in effectively detecting and addressing harmful behaviors, leaving users vulnerable to exploitation and trauma. Pin Lean argues that addressing this deficiency requires a multi-faceted approach that combines technological innovation, robust regulatory frameworks, and community-driven initiatives.

Pin Lean is an invited expert at this roundtable symposium, with other experts working at the nexus of feminist / queer theory, health and medicine, and technology / AI, to critically discuss current developments and concerns pertaining to the use of AI for medical gender equity.

In recent history, biomedical research has focused primarily on men, resulting in poorer health care for women and for non-binary, transgender and intersex people. Efforts to mitigate this bias are ongoing, but these continue to face the challenge of addressing diversity without essentializing. All too often, sex differences are still conceptualized as a strict binary, in isolation from gender, and without intersectional awareness. The use of AI techniques to advance medical equity must also grapple with this challenge. In particular, if medical AI is used to study gender/sex differences we must have a critical understanding of how human categories are understood and can become reified when such techniques are used, and what the implications of this might be both within and beyond the realm of health and medicine.