Disaggregating artificial intelligence biases: a law and systems engineering approach for AI governance and regulation

Emile Loza de Siles

Chapter Chapter 14 in Research Handbook on the Law of Artificial Intelligence, 2025, pp 275-306 from Edward Elgar Publishing

Abstract: Artificial intelligence (AI) bias, or so-called “algorithmic bias,” is a central focus of AI law and policy debates worldwide. Those discussions, however, have persisted, and erroneously so, in conceptualizing AI bias as a monolithic phenomenon and therefore capable of a relatively facile governance and regulatory approach. “One and done” seems to be the idea. AI biases, however, are many, complex, and often interoperating in their presentations throughout the AI lifecycle. A more scientific, process-oriented problem-solving approach to AI biases is needed to produce fact-based and actionable understandings with which to craft appropriate and effective AI governance and regulation regimes. This chapter applies a systems engineering approach to tease out and disaggregate the AI biases problem and render it actionable for governance, law, and policy. It profiles the AI biases problem space and its complexities. It conceives of AI as a human-machine enterprise with human accountability at its core. It then maps out the lifecycle for that joint human-machine enterprise bases as an ontological testbed for AI bias process control. Having synthesized bias literatures from machine learning, AI, computer science, behavioral economics, statistics, epidemiology, psychology, law, and other disciplines, this chapter offers the first treatment of thus far 50 AI biases as actionable subjects of AI law and policy. Combining its systems engineering and process control approach with its interdisciplinary interpretative and translational work, the chapter presents a taxonomy of six AI bias categories, explains how those categories relate to the AI human-machine lifecycle, identifies and categorizes a total of 50 AI biases with exemplar definitions, descriptions, and AI use case. A robust introduction to bias injection and other AI bias mechanisms follows.

Keywords: Data bias; Cognitive bias; Learning bias; Model bias; Societal bias; Use biases; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035316489
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