Building an AI Trust Score: A Data-Driven Framework to Evaluate Dataset Fitness

Abstract

The implementation of artificial intelligence in key business areas has heightened worries about the reliability and trust of decisions. The article presents the AI Trust Score framework, a detailed data-centric approach aimed at assessing the suitability of datasets for machine learning uses. Modern organizations encounter major issues due to scattered data sources, varying quality standards, and insufficient insight into data lineage throughout decentralized systems. Conventional data management methods fall short in meeting the specialized needs of AI model development, as the appropriateness of datasets goes beyond standard quality indicators to include bias identification, time relevance, and contextual suitability. The suggested framework creates an organized seven-dimensional evaluation model that includes dimensions of accuracy, completeness, freshness, bias risk, traceability, compliance, and contextual clarity. Every dimension undergoes a thorough assessment using standardized rubrics, allowing organizations to generate overall trust scores for specific datasets. Execution adheres to a structured five-phase approach that includes both automated and manual assessment elements to guarantee thorough coverage while preserving operational efficiency. Real-world uses in healthcare, insurance, and financial services show quantifiable enhancements in model dependability, adherence to regulations, and efficiency in operations. The framework enables a uniform assessment and ranking of data quality investments while defining explicit accountability structures for data stewardship duties.