Artificial intelligence, algorithmic governance, and social inequality in sport: a systematic literature review

Abstract

The accelerating integration of artificial intelligence (AI) and algorithmic decision systems into scouting, officiating, health monitoring, classification, and media representation has transformed athlete evaluation and governance, yet their implications for historically marginalised groups remain fragmented across disciplines. This systematic literature review examines how AI and algorithmic governance intersect with social inequality in sport, focusing on gender, race and ethnicity, disability and Para-sport, inclusion, accessibility, participation, and athlete rights. Following PRISMA 2020, a Scopus search identified 790 records; after screening and eligibility assessment, 10 studies met the inclusion criteria, with 50 additional sources providing contextual evidence. Eligible studies were English-language peer-reviewed articles and reviews published between 2016 and 2026. Two independent reviewers conducted screening and data extraction, achieving high inter-rater reliability (Cohen's κ = 0.86). Inductive thematic synthesis addressed how algorithmic governance shapes equity, reproduces bias, and informs governance responses. Four themes emerged: algorithmic talent identification may reinforce digital determinism and disadvantage under-represented athletes; AI-based health monitoring raises concerns regarding consent, autonomy, and sensitive data governance; machine-learning applications in Para-sport improve classification but remain constrained by limited datasets; and computational analyses reveal both discrimination and AI's capacity to detect it. The review proposes an equity-oriented framework and highlights priorities for explainable, auditable, participatory, and inclusive AI governance in sport.