Reinforcing Responsible AI: Provenance and Lineage Verification
DOI:
https://doi.org/10.15680/IJCTECE.2020.0302001Keywords:
Responsible AI, Data Provenance, Data Lineage, AI Governance, Transparency, Accountability, Auditability, Ethics in AI, Compliance, Model LifecycleAbstract
: As AI systems increasingly influence critical decisions in healthcare, finance, and justice, ensuring they are responsible becomes paramount. Provenance and lineage verification are emerging as essential mechanisms to establish transparency, trust, and accountability in AI systems. This paper examines the role of provenance (tracking the origin and ownership of data and models) and lineage (mapping data transformation over time) in reinforcing responsible AI. We review existing tools and methodologies, analyze their strengths and weaknesses, and propose an integrated verification framework aligned with ethical and regulatory standards. Our framework empowers organizations to meet governance requirements, conduct audits, and build public trust through verifiable AI workflows.
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