The set of practices governing the quality, representativeness and traceability of the data used to train and operate an AI system.
Data governance covers the rules and procedures by which an organisation controls the life cycle of its data, from collection to use, ensuring their quality, representativeness, relevance and traceability. For high-risk AI systems, the AI Act makes it an explicit obligation, because the reliability of a model never exceeds that of the data that trained it. Biased, incomplete or unrepresentative data produce algorithmic bias and, in insurance, risks of discrimination in pricing. Rigorous governance aims to detect and correct these flaws upstream, to document datasets and to ensure their compliance, notably with the GDPR. It thus constitutes one of the pillars of the regulatory compliance that has become a condition of insurance underwriting.
Before deploying a health pricing model, an insurer audits its training data to verify that no proxy variable introduces indirect discrimination, a data-governance requirement set by the AI Act.
data governance, qualité des données