Quality-Driven Governance Frameworks for Reliable and Compliant AI Systems Using Data Contract Architectures

Abstract

To guarantee the reliability/consistency and regulatory compliance of artificial intelligence systems, governance structures are needed that can impose quality constraints throughout the entire lifecycle of the data and model processes. The old forms of AI governance are based on manual inspection, compliance that is based on documentation and reactive compliance auditing which are inadequate in dynamic systems that constantly respond to real-time data streams. This paper presents a quality-centered governance model that makes use of Data Contract Architectures, programmable and enforceable interfaces among data producers, AI systems, and governance strata. Data contracts specify clear-cut quality conditions, validation conditions, compliance conditions, and operational conditions which may be automatically reviewed and implemented at the time of data ingestion, transformation and execution of model processes. The suggested framework brings together the architectural ideas of data engineering, quality assurance, and AI governance with the aim of facilitating transparent operations, responsible ones, and verifiable ones. The evaluation presented through experiments shows that there are enhanced data integrity, consistency, system stability, and traceability of compliance. This paper demonstrates that data contracts may be used to build viable and compliant AI systems and have the potential to maintain high-quality performance in response to changing regulatory and operational pressures.