Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence

TL;DR

This paper discusses the importance of human-guided training in AI to align with emerging management-based regulations, emphasizing human oversight to improve AI fairness, ethics, and performance.

Contribution

It highlights the connection between regulation and human oversight, advocating for refined human-guided training techniques in high-stakes AI applications.

Findings

Human-guided training can improve AI fairness and explainability.

Regulatory frameworks favor increased human oversight during AI training.

High-stakes AI use cases benefit more from human-guided training than data-only methods.

Abstract

Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have called a management-based approach to regulation. Recent initiatives in the United States and Europe, as well as the adoption of major self-regulatory standards by the International Organization for Standardization, share in common a core management-based paradigm. These management-based initiatives seek to motivate an increase in human oversight of how AI tools are trained and developed. Refinements and systematization of human-guided training techniques will thus be needed to fit within this emerging era of management-based regulatory paradigm. If taken seriously, human-guided training can alleviate some of the technical and ethical pressures on AI, boosting AI performance with human intuition as well as better addressing the needs for fairness and effective explainability. In this paper, we discuss the connection between the emerging management-based regulatory frameworks governing AI and the need for human oversight during training. We broadly cover some of the technical components involved in human-guided training and then argue that the kinds of high-stakes use cases for AI that appear of most concern to regulators should lean more on human-guided training than on data-only training. We hope to foster a discussion between legal scholars and computer scientists involving how to govern a domain of technology that is vast, heterogenous, and dynamic in its applications and risks.