DBSD: A Geometric and Regulatory Framework for Inference Bias in AI

Abstract

This article introduces the Deep Bias–Systematic Deviation (DBSD) framework, a novel theoretical, mathematical, and regulatory approach to understanding bias in contemporary artificial intelligence systems. Unlike conventional approaches that define bias through unequal outputs or statistical disparities, DBSD reconceptualizes bias as the degree of ease with which systematic deviation may be generated, inferred, and propagated through latent representational structures. The framework advances three primary contributions. First, it proposes an impedance-based model of bias, in which harmful inference corresponds to low resistance within information systems. Second, it develops the Tri-World Embedding Bias Model, representing the observed, true, and ideal worlds as vectors within embedding space and quantifying their relationships using cosine similarity. Third, it introduces a dynamic regulatory model in which intervention strength adapts continuously through feedback mechanisms governed by parameters such as λ and γ. By shifting analysis from output bias to inference bias, DBSD integrates philosophy, machine learning, vector geometry, and regulatory theory into a unified framework. It provides a new foundation for measuring, mitigating, and governing bias in large language models and other AI systems.