Taking Training Seriously: Human Guidance and Management-Based
  Regulation of Artificial Intelligence
Cary Coglianese Colton R. Crum
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.