Download PDFOpen PDF in browserTransparency and Accountability in AI/ML Regulatory Reporting: Explaining Algorithms and InterpretabilityEasyChair Preprint 126288 pages•Date: March 20, 2024AbstractArtificial Intelligence (AI) and Machine Learning (ML) algorithms are increasingly being integrated into various sectors, including finance, healthcare, and governance. However, their opaque nature poses challenges for ensuring accountability and regulatory compliance. This paper explores the significance of transparency and interpretability in AI/ML regulatory reporting, emphasizing the need for explaining algorithms and their outcomes. Through a comprehensive review of literature and case studies, this paper highlights the current landscape, challenges, and potential solutions for enhancing transparency and accountability in AI/ML systems. Additionally, it discusses regulatory frameworks and best practices to promote responsible AI deployment while balancing innovation and regulatory compliance. Keyphrases: Accountability, Artificial Intelligence, Explainable AI, Regulatory Reporting, machine learning, transparency
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