zerobias

Zerobias is a term used to describe efforts to reduce or eliminate bias in data, analysis, and decision making. It is used across disciplines such as statistics, machine learning, psychology, and organizational governance to denote a goal rather than a single method. In statistics, zerobias commonly refers to properties of estimators that have zero bias, meaning their expected value equals the true parameter. While zero bias is desirable, it does not guarantee low variance and must be considered alongside efficiency.

In machine learning and data science, zerobias is often invoked to describe approaches that remove unfair or harmful biases from models or data. This includes debiasing techniques in training data, fairness constraints during model optimization, and post hoc audits of model outputs. The concept is central to discussions of algorithmic fairness and responsible AI.

In industry and practice, zerobias can also appear as brand names or project titles for bias-audit tools and services that help organizations assess cognitive or statistical biases within processes such as hiring, lending, or content moderation. Proponents emphasize transparency, reproducibility, and continual monitoring as core components of zerobias efforts.

Etymology and usage vary by field, but the overarching idea is consistent: strive toward judgments, estimators, and systems whose biases are minimized, understood, and, where possible, corrected. See also bias, unbiased estimation, debiasing, and fairness in AI.