Inteligencia Artificial Generativa: Ética, Sesgos y Sostenibilidad

Abstract

Background: Generative Artificial Intelligence (GAI) represents one of the most profound and accelerated technological transformations in recent decades. Systems such as GPT-4, Claude, Gemini, DALL-E, Midjourney, and Stable Diffusion have demonstrated unprecedented capabilities to redefine the boundaries between human and automated production. However, this disruption unfolds upon historical structures of inequality, power concentration, and informational asymmetries that GAI models absorb, reproduce, and amplify.

Methodology: This research constitutes a systematic literature review of high-impact scientific literature, executed under the PRISMA 2020 protocol. The study analyzes 130 indexed academic sources published between 2016 and 2026, selected from an initial universe of 104,660 records across four international databases: IEEE Xplore, EBSCO Research, Scopus (Elsevier), and Web of Science (Clarivate). The investigation examines the influence of cognitive biases, training data quality, and environmental impact, with a sectoral emphasis on health and justice and a specific projection toward the Latin American context.

Results: The study identified six primary categories of bias: representation, measurement, evaluation, implementation, LLM-specific, and historical-structural. In healthcare, evidence shows that models trained on electronic records often confuse differential access to care with actual clinical needs. In justice, seemingly neutral variables serve as proxies for race, reproducing discrimination under technical objectivity. Regarding mitigation, while techniques like data rebalancing, adversarial learning, and frameworks (NoBIAS, EU AI Act) exist, their effectiveness is moderate and involves significant trade-offs; improving intergroup equity often results in a decrease of up to 10 percentage points in global accuracy.

Sustainability: "Green AI" strategies—such as model pruning, quantization, and knowledge distillation—can reduce energy consumption by 13% to 115%. However, only 23% of Green AI studies involve industrial partners, revealing a gap between technical knowledge and practical adoption.

Conclusions: Ethical and sustainable GAI development requires simultaneous interventions: binding regulatory frameworks, independent audits, and algorithmic transparency. For Latin America and Colombia, the challenge lies in overcoming "algorithmic transport failure"—the dependence on models from the Global North that fail to capture local linguistic, cultural, and socioeconomic nuances. Reversing this vulnerability requires not only technical investment but the construction of a unique epistemic perspective that positions the region as an active subject in the contemporary technological order.