Cary Coglianese & David Lehr - 105 Geo. L.J. 1147 (June 2017)
Direct PDF link: https://www.law.upenn.edu/live/files/63 ... t-penn-ileMachine-learning algorithms are transforming large segments of the economy as they fuel innovation in search engines, self-driving cars, product marketing, and medical imaging, among many other technologies. As machine learning's use expands across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others have clear potential in the near term to use algorithms to shape official decisions over both rulemaking and adjudication. It is no longer fanciful to envision a future in which government agencies could effectively make law by robot, a prospect that understandably conjures up dystopian images of individuals surrendering their liberty to the control of computerized overlords. Should society be alarmed by governmental use of machine-learning applications? We examine this question by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law. At first glance, the idea of algorithmic regulation might appear to offend one or more traditional doctrines, such as the nondelegation doctrine, procedural due process, equal protection, or principles of reason-giving and transparency. We conclude, however, that when machine-learning technology is properly understood, its use by government agencies can comfortably fit within these conventional legal parameters. We recognize, of course, that the legality of regulation by robot is only one criterion by which its use should be assessed. Agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law; in some cases, safeguards may be needed for machine learning to satisfy broader, good-governance aspirations. Yet, in contrast with the emerging alarmism, we resist any categorical dismissal of a future administrative state in which algorithmic automation guides, and even at times makes, key decisions. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism about the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just.
I'm skeptical...
