Computable Fundamental Rights Impact Assessment for Cross-Border High-Risk AI

Abstract

Artificial intelligence increasingly governs access to credit, employment, and identity verification, raising questions of rights protection when deployed across borders. This paper develops a computable framework for Fundamental Rights Impact Assessment (FRIA) that transforms the legal principles of necessity and proportionality into quantifiable metrics. By embedding these standards into algorithmic pipelines, the framework enables verifiable auditing of high-risk AI systems. Simulations were conducted in two domains, credit scoring and biometric authentication, using synthetic datasets modeled on European and non-European jurisdictions. The necessity audits reduced the average input set by 24.6 ± 2.3 variables while sustaining predictive accuracy, while proportionality assessments exposed heavy reliance on sensitive features in 39%* of credit scoring models and significant subgroup disparities in biometric authentication. Distributed verification protocols preserved results on blockchain ledgers, ensuring transparency and cross-border accountability. The findings demonstrate that computable FRIAs can operationalize fundamental rights obligations, producing results that can be inspected by regulators and reviewed in courts. The study concludes that computable methods offer a practical bridge between jurisprudential principles and algorithmic implementation, though persistent divergences in cross-border proportionality standards remain a major challenge for harmonized enforcement.