What question did this study set out to answer?

The study aims to establish a framework for making retrain-or-recalibrate decisions in AI systems while considering various costs and reliability issues.

June 17, 2026Open Access

Governing the Retrain-or-Recalibrate Decision: Reliability Volatility, Carbon Cost, and Auditability across the AI Lifecycle

Key Points

The study aims to establish a framework for making retrain-or-recalibrate decisions in AI systems while considering various costs and reliability issues.
Introduced a governance-oriented framework for retraining or recalibrating models.
Developed design requirements and a maturity model for AI lifecycle governance.
Proposed an audit-oriented decision structure to monitor factors like discrimination and distribution drift.
Establishes the importance of accounting for reliability volatility and operational costs in AI governance.
Highlights the necessity of an auditable approach to ensure accountability in AI decisions.
Identifies key parameters like carbon cost that affect the sustainability of AI systems.

Abstract

This preprint proposes a governance-oriented framework for the retrain-or-recalibrate decision in high-stakes machine learning systems. Building on prior work on deployment reliability under temporal distribution shift, the paper reframes model maintenance as an auditable organisational decision involving reliability volatility, operational cost, carbon cost, and lifecycle accountability. The paper introduces design requirements, a maturity model, and an audit-oriented decision structure for monitoring discrimination, calibration, distributional drift, and explanation stability over time. The contribution is positioned as a design science artefact for responsible and sustainable AI lifecycle governance.

Governing the Retrain-or-Recalibrate Decision: Reliability Volatility, Carbon Cost, and Auditability across the AI Lifecycle

Key Points

Abstract

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