AI without representation is just inequity at scale: on the exportation of unrepresentative artificial intelligence models to the Global South

Tinco Aliaga C.A.; Tan M.J.T.; Hinostroza Fuentes V.G.; Abdul Karim H.; AlDahoul N. · Frontiers in Artificial Intelligence (2026) · DOI: 10.3389/frai.2026.1894116

Resumen

A common assumption in discussions of artificial intelligence (AI) is that increasing demographic representation in training data is sufficient to mitigate bias. Under this view, failures in facial recognition, clinical classification, and language generation are treated primarily as problems of coverage that can be resolved through more inclusive sampling. While intuitively appealing, this assumption becomes problematic when models trained and validated within one sociotechnical context are deployed into others whose realities were never meaningfully represented during development. This limitation is particularly evident in health-related AI, where outcomes are shaped not only by biological variables but also by lifestyle and environmental factors that interact dynamically over time, challenging purely data-centric notions of representation (Tan et al., 2025). Copyright

Instituciones: Universidad de Ingeniería y Tecnología