Experts Warn AI Algorithms Lack Neutrality Due to Human Bias

Experts argue that artificial intelligence algorithms are not neutral because they are trained on data produced by humans [1, 2, 3].

This lack of neutrality matters because AI systems are increasingly integrated into global decision-making processes. If these systems embed invisible biases, they may automate and scale existing cultural or ideological prejudices without oversight.

Commentator Wario Duckerman and specialists cited by La Nación said that algorithms embed human cultural and ideological biases [1, 2]. Because AI learns from human-generated data, true neutrality is impossible [1, 2]. These biases are not errors in the code but reflections of the data sources themselves.

The debate has gained momentum this year across the U.S., Europe, and China ^[2]. In Spain, the discussion reached a local level during a breakfast event in Gijón, Asturias. More than 80 people ^[4] attended the gathering to discuss the necessity of setting limits on artificial intelligence and maintaining a healthy skepticism toward algorithmic outputs.

Other reports from Latin America and the Caribbean highlight that the intersection of ethics and technical capacity is a primary concern for the region ^[3]. The ongoing dialogue suggests that the "invisible" nature of these biases makes them particularly dangerous, as users often assume a machine is objective ^[1].

Regulatory discussions in 2026 have focused on how ethics and regulation can shape the objectives of AI development ^[5]. Experts said that recognizing the myth of neutrality is the first step toward creating more transparent systems.