Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design

21 Pages Posted: 21 Oct 2020

See all articles by Arsh Shah

Arsh Shah

Sydney Law School; Lake Forest College - Department of Economics

Date Written: May 27, 2020

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).

Keywords: AI, explainability, transparency, accountability, FAT Forensics, Github, artificial intelligence, self-regulation, AI design, regulatory design, AI regulation, AI legislation, emerging technology

Suggested Citation

Shah, Arsh, Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design (May 27, 2020). Available at SSRN: https://ssrn.com/abstract=3617349 or http://dx.doi.org/10.2139/ssrn.3617349

Arsh Shah (Contact Author)

Sydney Law School ( email )

Faculty of Law Building, F10
Sydney, NSW
Australia

Lake Forest College - Department of Economics ( email )

555 N. Sheridan Road
Lake Forest, IL 60045
United States

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