From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems

Moving beyond correlation to true independence: Unlike existing bias mitigation methods that only reduce statistical correlation with sensitive attributes, this framework uses mathematical guarantees (optimal transport theory) to ensure AI systems are truly independent of protected characteristics and their hidden proxies—meaning a loan approval system couldn't infer applicant race even indirectly through variables like ZIP code.

Formal identification of hidden bias sources: The framework uses ontology engineering (a structured knowledge representation system) to systematically map out not just obvious sensitive attributes like race or gender, but also their "proxies"—seemingly neutral variables that actually reveal protected information—providing a more complete picture of where bias can hide in AI systems.

Measurable fairness guarantees: By translating ethical principles into formal mathematical structures, this approach moves AI fairness from aspirational declarations to provable certifications, allowing organizations to demonstrate with mathematical rigor that their systems meet fairness standards rather than relying on post-hoc audits or claims.

Preserving accuracy while ensuring fairness: The optimal transport method balances fairness with performance by finding the minimal changes needed to remove bias, rather than creating overly simplified systems that sacrifice usefulness—important for real-world adoption where stakeholders worry fair AI means worse predictions.

Practical application to high-stakes decisions: The framework is designed for consequential domains like loan approval where hidden proxies for protected characteristics can perpetuate discrimination, offering a concrete path toward trustworthy AI in financial services and similar sectors.