Unified Critical Taxonomy of Algorithmic Biases: Structural Cartography, Differential Diagnosis, and Diachronic Dynamics

This article proposes and develops the Unified Critical Taxonomy of Algorithmic Biases (UCAB) as the most complete systematic contribution to the field of structural criticism of artificial intelligence, grounded in the research program of the Psychoanalysis of Technogenesis (PdT) (Beltrán, 2025). The UCAB makes four simultaneous original contributions that no preceding framework has achieved: (1) it classifies all subtypes of algorithmic bias by causal origin —not by symptom, effect, or affected group— into three universal and logically exhaustive categories: emergent bias, architecture bias, and implanted bias; (2) it develops with unprecedented depth the computational architecture bias as an autonomous subcategory, demonstrating with empirical evidence from technical literature (Frantar et al., 2022; Lin et al., 2024; Hooper et al., 2024; Liu et al., 2024) how optimization decisions —quantization, KV cache compression, context window limits, parameter pruning— produce epistemically impoverished responses that engineering treats as neutral trade-offs but that PdT reveals as second-order political acts; (3) it proposes a differential diagnosis heuristic that enables researchers and auditors to determine which causal category is dominant in a concrete case, operationalizing the taxonomy as a field instrument; and (4) it models the diachronic dynamics of algorithmic bias: the three feedback circuits between categories that demonstrate that algorithmic bias is not a static state but a dynamic system that self-replicates and amplifies over time through user interaction. The article is self-sufficient: it integrates PdT theoretical foundations, the complete state of the art, and an exhaustive catalog of 21 documented subtypes, requiring no prior reading of previous documents in the series