Regulating Machine Learning by Design

The regulation of digital technologies around the world draws from various regulatory techniques. One such technique is regulation by design, in which regulation specify requirements that software designers must follow when creating any systems. This paper examines the suitability of regulation by design approaches to machine learning, arguing that they are potentially useful but have a narrow scope of application. Drawing from EU law examples, it shows how regulation by design relies on the delegation of normative definitions and enforcement to software designers, but such delegation is only effective if a few conditions are present. These conditions, however, are seldom met by applications of machine learning technologies in the real world, and so regulation by design cannot address many of the pressing concerns driving regulation. Nonetheless, by-design provisions can support regulation if applied to well-defined problems that lend themselves to clear expression in software code. Hence, regulation by design, within its proper limits, can be a powerful tool for regulators of machine learning technologies.

By Marco Almada^[1]

I. INTRODUCTION

Machine learning (“ML”) is a new frontier for regulation. Little more than a decade ago, ML-based technologies were a niche concern even in the field of technology regulation, as the field of artificial intelligence (“AI”) lingered on at a low point of investment. Move forward a decade, and the situation could not be mor