recalCABais

RecalCABais is a theoretical framework in artificial intelligence for the dynamic recalibration of cognitive biases in machine decision-making. The term is a portmanteau of recalibrate and cognitive biases, with stylized capitalization to reflect its core components.

Definition: It describes a modular process in which a bias detector monitors outputs for indicators of bias across demographics, contexts, or tasks. A calibration engine assigns weights to detected bias signals and updates model parameters through a feedback loop, with the goal of reducing unfair or undesired biases while preserving accuracy.

Mechanism: The framework relies on three elements: a bias detector, a calibration scheduler, and a governance layer. The detector analyzes inputs and outputs against defined fairness metrics. The scheduler adjusts bias weights using Bayesian updating or reinforcement signals. The governance layer enforces constraints to prevent overcorrection or metric manipulation.

History and usage: The concept emerged in theoretical AI ethics discussions in the 2020s and is used in academic simulations and policy debates as a thought experiment about how to balance fairness objectives with performance. It is not a standardized method in industry today.

Impact and criticism: Proponents argue it provides a transparent schema for bias adjustment and auditability. Critics warn that recalibration can mask underlying data representation issues or be exploited to game metrics, and that governance is essential to prevent instability.

See also: algorithmic fairness, bias mitigation, model auditing, calibration.