Establishing Trust in AI-Driven Data Observability and Quality Control: A Framework for Reliable and Scalable Standards
Behrouz Banitalebi, Satya Venkata Anusha Dwivedula · 2025
The increasing reliance on Artificial Intelligence(AI) for data observability and quality control (QC) necessitates robust standards to ensure trustworthiness, reliability, and scalability. This paper introduces a detailed AI-driven data quality framework that integrates critical components such as data lineage tracking, interoperability standards, decentralized pipeline architecture, governance, and human-in-the-loop validation. Through this layered approach, the framework ensures scalability, traceability, and compliance, enhancing the trustworthiness of AI systems in production environments. We propose Data Trust Score (DTS) - a candidate IEEE-standard metric that quantifies trustworthiness through three pillars: Accuracy & Reliability, Explainability & Traceability, and Ethical & Governance Compliance. We showcase a comparative analysis with existing standards ISO/IEC 25012, NIST AI RMF, and IEEE P7003, illustrating the strengths of the proposed framework across scalability, real-time processing, explainability, and compliance readiness dimensions. The score supports progressive organizational adoption through integration with the Gartner AI Maturity Model. This work provides practical recommendations for evaluating AI-driven data observability systems across various industries.