Artificial Intelligence and LGBTQI+phobia: Algorithms of Exclusion or Tools of Inclusion?

Abstract

Introduction: Artificial intelligence (AI) systems increasingly mediate online speech, shaping visibility, participation, and recognition in digital public spaces. For LGBTQI+ communities, AI-based content moderation operates within contexts marked by persistent homophobia and transphobia, raising concerns about bias, exclusion, and uneven protection from harm. This article examines the emerging scientific literature on AI-based moderation of LGBTQI+phobic discourse through the lens of sociotechnical governance. Methods: The study employs a bibliometric and sociotechnical mapping approach guided by the PRISMA 2021 framework. A systematic search of the Scopus database identified 23 peer-reviewed publications published between 2020 and 2025. Bibliometric indicators, co-authorship and keyword network analyses were combined with a critical sociotechnical perspective to examine the intellectual structure, epistemic authority, methodological trends, and themes characterising sociotechnical governance in this emerging research field. Results: Findings indicate a highly concentrated research field dominated by a small number of countries, institutions, and computational disciplines, reinforcing geopolitical and linguistic asymmetries in scientific production. The literature indicates an increasing research focus on large language models, explainable AI, and counter-narrative generation, reflecting a shift in scholarly attention from hate speech detection to broader approaches to AI-enabled content governance. However, links between AI moderation, mental health, stigma, and well-being remain weakly integrated, and participatory approaches are scarce. Conclusions: The bibliometric evidence indicates that the emerging scientific literature increasingly conceptualises AI-based moderation as a form of digital governance with potential implications for LGBTQI+ communities. The mapped literature also highlights persistent concerns regarding accountability, inclusiveness, and interdisciplinary integration, suggesting important directions for future research and policy development. Policy Implications: The mapped literature suggests that future policies should promote transparent, auditable, and participatory AI governance, support multilingual and culturally inclusive datasets, and integrate content moderation into broader digital public health and anti-discrimination strategies.