AI Meets Accountability: Data Provenance in FATE Frameworks
DOI:
https://doi.org/10.15680/1vvdc534Keywords:
AI, Accountability, Data Provenance, FATE Frameworks, Fairness, Transparency, Ethics, Data Lineage, Ethical AI, Machine Learning, Algorithmic Accountability, GovernanceAbstract
The rapid growth of Artificial Intelligence (AI) has led to profound changes across industries, from healthcare and finance to transportation and education. As AI-driven systems become increasingly integrated into decision-making processes, ensuring accountability and transparency in their operations is crucial. The FATE (Fairness, Accountability, Transparency, and Ethics) framework has emerged as a critical tool to assess the ethical dimensions of AI systems, but the lack of robust tracking mechanisms for data flow and transformation often undermines its effectiveness. This paper explores the intersection of data provenance and FATE frameworks, emphasizing the importance of data lineage in ensuring AI systems are transparent, accountable, and fair. We propose an AI-powered solution to enhance data provenance in FATE assessments, enabling real-time tracking of data from source to output. This solution ensures that AI decisions are traceable, audit-ready, and aligned with ethical standards, improving trust and compliance across various domains.
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