Provenanceandexplainability

Provenanceandexplainability is an emerging concept at the intersection of data management, artificial intelligence, and governance. It describes efforts to render automated decisions transparent by jointly addressing data provenance and model explainability. Data provenance, in this context, covers the origin of data, its custody, and the full lineage of transformations from source to model input, including versions, quality attributes, and audit trails. Explainability focuses on why a model produced a given prediction or decision, through local explanations for individual cases and global explanations of model behavior, using methods such as feature attribution, counterfactuals, and interpretable surrogates.

By combining provenance with explainability, organizations can trace a decision back to its data inputs, transformations, and model logic, enabling reproducibility, debugging, and accountability. This integration supports governance, risk management, and regulatory compliance by providing auditable records of data origin, processing steps, and rationale behind outcomes.

Common techniques include data versioning and lineage graphs, audit logs, and integration with XAI methods (SHAP, LIME, counterfactual explanations). Processes emphasize end-to-end traceability, from data collection through feature engineering to prediction deployment.

Applications span finance, healthcare, hiring, and security, where transparency is valued or required. Proponents argue it improves trust and accountability while enabling detection of biases, data leakage, or data drift.

Challenges include scaling lineage tracking to large pipelines, preserving privacy in provenance data, dynamic datasets, incomplete or noisy records, and a lack of standardized metrics or frameworks. Ongoing work aims to harmonize standards, interoperable tooling, and best practices for auditing and governance.

The term reflects a broader trend toward responsible AI, integrating data-lineage accountability with explanation to support decisions that are both effective and accountable.