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Transparency and Accountability in AI/ML Regulatory Reporting: Explaining Algorithms and Interpretability

EasyChair Preprint no. 12628

8 pagesDate: March 20, 2024


Artificial 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, machine learning, Regulatory Reporting, transparency

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Michio Orio and Kiswah Noor},
  title = {Transparency and Accountability in AI/ML Regulatory Reporting: Explaining Algorithms and Interpretability},
  howpublished = {EasyChair Preprint no. 12628},

  year = {EasyChair, 2024}}
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