Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design

Abstract

This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).