Beyond Algorithms: Toward a Normative Theory of Automated Regulation
62 Boston College Law Review 1 (2021)
Texas A&M University School of Law Legal Studies Research Paper No. 20-23
59 Pages Posted: 25 Jun 2020 Last revised: 6 Apr 2021
Date Written: June 1, 2020
The proliferation of artificial intelligence in our daily lives has spawned a burgeoning literature on the dawn of dehumanized, algorithmic governance. Remarkably, the scholarly discourse overwhelmingly fails to acknowledge that automated, non-human governance has long been a reality. For more than a century, policymakers have relied on regulations that automatically adjust to changing circumstances, without the need for human intervention. This article surveys the track record of self-adjusting governance mechanisms to propose a normative theory of automated regulation.
Effective policymaking frequently requires anticipation of future developments, from technology innovation to geopolitical change. Self-adjusting regulation offers an insurance policy against the well-documented inaccuracies of even the most expert forecasts, reducing the need for costly and time-consuming administrative proceedings. Careful analysis of empirical evidence, existing literature, and precedent reveals that the benefits of regulatory automation extend well beyond mitigating regulatory inertia. From a political economy perspective, automated regulation can accommodate a wide range of competing beliefs and assumptions about the future to serve as a catalyst for more consensual policymaking. Public choice theory suggests that the same innate diversity of potential outcomes makes regulatory automation a natural antidote to the domination of special interests in the policymaking process.
Today’s automated regulations rely on relatively simplistic algebra, a far cry from the multivariate calculus behind smart algorithms. Harnessing the advanced math and greater predictive powers of artificial intelligence could provide a significant upgrade for the next generation of automated regulation. Any gains in mathematical sophistication, however, will likely come at a cost if the widespread scholarly skepticism toward algorithmic governance is any indication of future backlash and litigation. Policymakers should consider carefully whether their objectives may not be served as well, if not better, through more simplistic, but well-established methods of regulatory automation.
Keywords: algorithmic governance, accountability, law and technology, transparency, automation, climate change, public choice, energy, public utility, artificial intelligence, machine learning, gridlock
JEL Classification: K10, K32, K32, K40, O30, O38, Q20, Q47, Q48, Q50
Suggested Citation: Suggested Citation