Gilman & Wagman on The Law and Economics of Privacy

Daniel J. Gilman (International Center for Law & Economics) & Liad Wagman (Illinois Institute of Technology – Stuart School of Business) have posted “The Law and Economics of Privacy” on SSRN. Here is the abstract:

Consumer welfare has been a north star of the Federal Trade Commission (FTC), providing an organizing principle for diverse issues under the Commission’s dual competition and consumer protection missions and, specifically, a uniform ground on which to examine the law and economics of privacy matters and the tradeoffs that privacy policies entail. This paper provides the first contemporary literature synthesis by former FTC staff that brings together the legal and economics literatures on privacy. Our observations are the following: (a) privacy is a complex subject, not a simple attribute of goods and services or a simple state of affairs; (b) privacy policies entail complex tradeoffs for and across individuals; (c) the economic literature finds diverse effects, both intended and unintended, of privacy policies, including on competition and innovation; (d) while there is diverse and growing evidence of the costs of privacy policies, countervailing benefits have been understudied and, as of yet, empirical evidence of such benefits remains slight; and (e) observed costs associated with omnibus policies suggest caution regarding one-size-fits-all regulation.

Porat on Algorithmic Personalized Pricing

Haggai Porat (Harvard Law; Tel Aviv Economics) has posted “Algorithmic Personalized Pricing in the United States: A Legal Void” (in Cambridge Handbook on Price Personalization and the Law) on SSRN. Here is the abstract:

The United States is the Wild West of algorithmic personalized pricing. It is practiced (and researched) extensively, possibly more than anywhere else in the world, and at the same time, it is less regulated than in many of the jurisdictions surveyed in this Handbook, most notably the EU and China. This is not necessarily puzzling. American corporations have been the driving force behind many of the technological innovations associated with the rise and development of algorithmic personalized pricing. However, there is a long tradition in the US of opposition to regulating markets, and algorithmic personalized pricing exemplifies this approach. On this background, the goal of this Chapter is twofold.
First, the Chapter considers legal rules from various fields that can be used to regulate algorithmic personalized pricing. In mapping out and analyzing these rules, a primary aim of this Chapter is to demonstrate that many legal rules designed for seemingly unrelated purposes are, in fact, often well-suited to regulating algorithmic price personalization, with specific focus on antitrust law, consumer contracts law, and data protection law. While these legal fields have evolved, respectively, to protect competition, regulate consumers’ access to information, and protect consumers’ privacy (“data subjects,” in European terminology), each arguably has the potential to improve how the US legal system contends with algorithmic personalized pricing.
Second, using economic analysis, the Chapter seeks to develop analytical approaches to understanding how the legal rules it considers can be expected to affect algorithmic personalized pricing in ways that may not be immediately apparent. The analysis demonstrates the importance of understanding the economic (and technological) foundations of the phenomenon as well as the rules that regulate it. It is important to note that economic analysis here is not aimed at a normative evaluation of the extent to which the law should regulate algorithmic personalized pricing, which is a stance this Chapter refrains from taking given the theoretical and empirical ambiguity surrounding the welfare implications of algorithmic personalized pricing. Instead, focus is set on the potential effectiveness of certain legal rules for regulating algorithmic personalized pricing to any desired extent, without making any assertions about what that extent should be. Specifically, the Chapter demonstrates how economic analysis can inform two main lines of inquiry: first, whether a legal rule applies to algorithmic personalized pricing given the conditions stipulated by the former and the characteristics of the latter; and, second, how the legal rule, if applied, can be expected to affect sellers’ ability to engage in algorithmic personalized pricing. As such, the analysis attempts to develop the strongest claims for both sides of the debate over whether algorithmic personalized pricing should be limited or expanded.

Coglianese & Shaikh on AI Impact Assessment

Cary Coglianese (U Penn Law) and Nabil Shaikh (U Penn Law) have posted “Management-Based Oversight of the Automated State: Emerging Standards for AI Impact Assessment and Auditing in the Public Sector” in Yaghmaei, et al., eds., Global Perspectives on AI Impact Assessment (Oxford University Press, forthcoming 2024) on SSRN. Here is the abstract:

This paper focuses on the role for algorithmic auditing and impact assessment as a management-based approach to governing uses of artificial intelligence (AI) by government agencies. Because these uses can vary widely, as can their purposes, contexts, designs, and impacts, the responsible use of AI almost never can depend on compliance with a set of formulaic rules governing specific actions that agencies must take or outcomes to be avoided. Rather, AI governance will depend on adherence to a set of management-based standards that call for measures involving testing, validation, and monitoring throughout the lifecycle of AI design, development, and deployment. These measures of impact assessment and auditing will necessarily play a key role in helping to ensure that AI uses accord with principles of responsible AI. Already, governmental auditors from around the world have developed a series of frameworks and standards for auditing governmental use of AI. This paper describes and compares the main elements of such an approach by reference to standards issued in the United States, Canada, and Europe. The paper offers a synthesis of the main elements of AI impact assessment in the public sector and explores what is realistic to expect from the use of impact assessment and auditing as a tool for governing public sector AI.