A computational thinking approach for law and artificial intelligence

Woodrow Barfield

Chapter Chapter 1 in Research Handbook on the Law of Artificial Intelligence, 2025, pp 2-12 from Edward Elgar Publishing

Abstract: The chapter summarizes concepts and principles in computing which I propose are essential to understanding future directions in artificial intelligence (AI) and contributing to the development of autonomous or semi-autonomous systems which are creating challenges to our laws, statutes, and regulations. Basically, at its core, the infrastructure of AI revolves around the ability of a system to compute. Therefore, I propose that legal scholars and legislators should embrace a computational thinking approach to better understand how AI systems operate and to increase their understanding of how the law will be impacted by continuing advances in AI. From this approach stems the idea that computational resources are critical for the development of AI-enabled systems which offer unique and interesting challenges to our legal systems. Further, I argue that computational resources are essential to create the “thinking infrastructure” necessary for AI to continue its rapid development, which, among others, will lead to AI-controlled systems that make decisions independent of human input or supervisory control. In this chapter, I propose that basic knowledge of key concepts in computing is necessary to effectively regulate AI systems, to enact laws and statutes that will be relevant for evolving AI systems, and to anticipate future developments in AI which will offer unique challenges to Constitutional, tort, criminal, contract, human rights, and other areas of law. For the above reasons, I propose that a computational thinking approach is necessary to increase our understanding of where AI is headed and how the law will be impacted by more powerful and autonomous AI systems.

Keywords: Computational resources; Moore’s Law; Law of accelerating returns; Three laws of robotics; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035316489
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