Pubblicazioni

Why should regulators be independent? How is it possible to reduce the gap between de iure and de facto independence? This OECD guidance addresses this topic by analysing the five dimensions in which the “culture of independence” should spread: role clarity and responsibility, transparency and accountability, financial independence, independence of leadership, and staff behaviour (Maria Bianca Armiento, Rassegna Osservatorio Air, n. 4/2019).

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded 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 the 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. Obviously, agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law, and 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 key decisions are guided by, and even at times made by, algorithmic automation. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism over the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just.

The next generation of e-commerce will be conducted by digital agents, based on algorithms that will not only make purchase recommendations, but will also predict what we want, make purchase decisions, negotiate and execute the transaction for the consumers, and even automatically form coalitions of buyers to enjoy better terms, thereby replacing human decision-making. Algorithmic consumers have the potential to change dramatically the way we conduct business, raising new conceptual and regulatory challenges. This game-changing technological development has significant implications for regulation, which should be adjusted to a reality of consumers making their purchase decisions via algorithms. Despite this challenge, scholarship addressing commercial algorithms focused primarily on the use of algorithms by suppliers. This article seeks to fill this void. We first explore the technological advances which are shaping algorithmic consumers, and analyze how these advances affect the competitive dynamic in the market. Then we analyze the implications of such technological advances on regulation, identifying three main challenges.

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