Witt on The Digital Markets Act 

Anne Witt (EDHEC Business School – Department of Legal Sciences) has posted “The Digital Markets Act – Regulating the Wild West” (Common Market Law Review, Forthcoming 2023) on SSRN. Here is the abstract:

This contribution critically assesses the European Union’s Digital Markets Act (DMA). The DMA is the first comprehensive legal regime to regulate digital gatekeepers in the aim of making platforms markets fairer and more contestable. To this end, the DMA establishes 22 per se conduct rules for designated platforms. It also precludes national gatekeeper regulation by EU Member States, thereby calling into question the legality of the pioneering German sec. 19a GWB. The analysis shows that the DMA’s rules are not as rigid as they may appear at first sight. While it is more accepting of false positives than of false negatives, the DMA contains several corrective mechanisms that could allow the Commission to finetune the rules to address both the danger of over- and under-inclusiveness. A further positive is that the new regulation incorporates key concepts of the GDPR, and requires coordination between the Commission and key EU data protection bodies. On the downside, the DMA does not contain any substantive principles for the assessment of gatekeeper acquisitions, leaving a worrying gap. While the DMA’s conduct rules outlaw specific leveraging strategies in digital ecosystems and may thereby indirectly address certain non-horizontal concerns arising from gatekeeper acquisitions, it remains that the European Union’s existing guidance on merger control is seriously out of date. The merger guidelines therefore urgently need updating to include (workable) theories of harm for concentrations in the digital economy.

Bar-Gill, Sunstein & Talgam-Cohen on Algorithmic Harm in Consumer Markets

Oren Bar-Gill (Harvard Law), Cass R. Sunstein (Harvard Law; Harvard Kennedy School), and Inbal Talgam-Cohen (Technion-Israel Institute of Technology) have posted “Algorithmic Harm in Consumer Markets” on SSRN. Here is the abstract:

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individualization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can disproportionately affect members of identifiable groups, including women and people of color. Since algorithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency—transparency about the nature, uses, and consequences of algorithms—is both crucial and challenging; novel methods designed to open the algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on exploitation of an absence of information or of behavioral biases.

Almada & Petit on The EU AI Act: Between Product Safety and Fundamental Rights

Marco Almada (EUI Law) and Nicolas Petit (same) have posted “The EU AI Act: Between Product Safety and Fundamental Rights” on SSRN. Here is the abstract:

The European Union (“EU”) Artificial Intelligence Act (the AI Act) is a legal medley. Under the banner of risk-based regulation, the AI Act combines two repertoires of European Union (EU) law, namely product safety and fundamental rights protection. Like a medley, the AI Act attempts to combine the best features of both repertoires. But like a medley, the AI Act risks delivering insufficient levels of both product safety or fundamental rights protection. This article describes these issues by reference to three classical issues of law and technology. Some adjustments to the text and spirit of the AI Act are suggested.

Zingales & Renzetti on Digital Platform Ecosystems and Conglomerate Mergers: A Review of the Brazilian Experience

Nicolo Zingales (Getulio Vargas Foundation (FGV); Tilburg Law and Economics Center (TILEC); Stanford University – Stanford Law School Center for Internet and Society) and Bruno Renzetti (Yale University, Law School; University of Sao Paulo (USP), Faculty of Law (FD)) have posted “Digital Platform Ecosystems and Conglomerate Mergers: A Review of the Brazilian Experience” (World Competition 45 (4) (2022)) on SSRN. Here is the abstract:

This paper highlights some of the key challenges for the Brazilian merger control regime in dealing with mergers involving digital platform ecosystems (DPEs). After a quick introduction to DPEs, we illustrate how conglomerate effects that are raised by such mergers remain largely unaddressed in the current landscape for merger control in Brazil. The paper is divided in four sections. First, we introduce the reader to the framework for merger control in Brazil. Second, we identify the possible theories of harm related to conglomerate mergers, and elaborate on the way in which their application may be affected by the context of DPEs. Third, we conduct a review of previous mergers involving DPEs in Brazil, aiming to identify the theories of harm employed (and those that could have been explored) in each case. Fourth and finally, we summarize and results and suggest adaptations to the current regime, advancing proposals for a more consistent and predictable analysis.

Porat on Behavior-Based Price Discrimination and Consumer Protection in the Age of Algorithms

Haggai Porat (Harvard Law School; Tel Aviv University School of Economics) has posted “Behavior-Based Price Discrimination and Consumer Protection in the Age of Algorithms” on SSRN. Here is the abstract:

The legal literature on price discrimination focuses primarily on consumers’ immutable features, like when higher interest rates are offered to black borrowers and higher prices to women at car dealerships. This paper examines a different type of discriminatory pricing practice: behavior-based pricing (BBP), where prices are set based on consumers’ behavior, most prominently their prior purchasing decisions. The increased use of artificial intelligence and machine learning algorithms to set prices has facilitated the growing penetration of BBP in various markets. Unlike race-based and sex-based discrimination, with BBP, consumers can strategically adjust their behavior to impact the prices they will be offered in the future. Sellers, in turn, can adjust prices in early periods to influence consumers’ purchasing decisions so as to increase the informational value of these decisions and thereby maximize profits. This paper analyzes possible legal responses to BBP and arrives at three surprising policy implications: One, when non-BBP discrimination is efficient but with potentially problematic distributional implications, BBP can either increase or decrease efficiency. Two, even if BBP is desirable, mandating its disclosure may reduce overall welfare even though this would reduce informational asymmetry in the market. Three, a right to be forgotten (a right to erasure) may be desirable even though it increases informational asymmetry.

Schrepel on The Making of An Antitrust API: Proof of Concept

Thibault Schrepel (VU Amsterdam; Stanford Codex Center; Sorbonne; Sciences Po) has posted “The Making of An Antitrust API: Proof of Concept” (Stanford University CodeX Research Paper Series 2022) on SSRN. Here is the abstract:

Computational antitrust promises not only to help antitrust agencies preside over increasingly complex and dynamic markets, but also to provide companies with the tools to assess and enforce compliance with antitrust laws. If research in the space has been primarily dedicated to supporting antitrust agencies, this article fills the gap by offering an innovative solution for companies. Specifically, this article serves as a proof of concept whose aim is to guide antitrust agencies in creating a decision-trees-based antitrust compliance API intended for market players. It includes an open access prototype that automates compliance with Article 102 TFEU, discusses its limitations and lessons to be learned.

Colangelo on European Proposal for a Data Act – A First Assessment

Giuseppe Colangelo (University of Basilicata; Stanford Law School; LUISS) has posted “European Proposal for a Data Act – A First Assessment” (CERRE Evaluation Paper 2022) on SSRN. Here is the abstract:

On 23 February 2022, the European Commission unveiled its proposal for a Data Act (DA). As declared in the Impact Assessment, the DA complements two other major instruments shaping the European single market for data, such as the Data Governance Act and the Digital Markets Act (DMA), and is a key pillar of the European Strategy for Data in which the Commission announced the establishment of EU-wide common, interoperable data spaces in strategic sectors to overcome legal and technical barriers to data sharing.

To contribute to the current policy debate, the paper provides a first assessment of the tabled DA and will suggest possible improvements for the ongoing legislative negotiations.

Li on Affinity-Based Algorithmic Pricing: A Dilemma for EU Data Protection Law

Zihao Li (University of Glasgow) has posted “Affinity-Based Algorithmic Pricing: A Dilemma for EU Data Protection Law” (Computer Law & Security Review, Volume 46, 2022) on SSRN. Here is the abstract:

The emergence of big data and machine learning has allowed sellers and online platforms to tailor pricing for customers in real-time, but as many legal scholars have pointed out, personalised pricing poses a threat to the fundamental values of privacy and non-discrimination, raising legal and ethical concerns. However, most of those studies neglect affinity-based algorithmic pricing, which may bypass the General Data Protection Regulation (GDPR). This paper evaluates current data protection law in Europe against online algorithmic pricing. The first contribution of the paper is to introduce and clarify the term “online algorithmic pricing” in the context of data protection legal studies, as well as a new taxonomy of online algorithmic pricing by processing the data types. In doing so, the paper finds that the legal nature of affinity data is hard to classify as personal data. Therefore, affinity-based algorithmic pricing is highly likely to circumvent the GDPR. The second contribution of the paper is that it points out that even though some types of online algorithmic pricing can be covered by the GDPR, the data rights provided by the GDPR struggle to provide substantial help. The key finding of this paper is that the GDPR fails to apply to affinity-based algorithmic pricing, but the latter still can lead to privacy invasion. Therefore, four potential resolutions are raised, relating to group privacy, the remit of data protection law, the ex-ante measures in data protection, and a more comprehensive regulatory approach.

Schrepel & Goroza on The Adoption of Computational Antitrust by Agencies: 2021 Report

Thibault Schrepel (University Paris 1 Panthéon-Sorbonne; VU University Amsterdam; Stanford University’s Codex Center; Sciences Po) and Teodora Groza (Sciences Po Law School) have posted “The Adoption of Computational Antitrust by Agencies: 2021 Report” (2 Stanford Computational Antitrust, 78 (2022)) on SSRN. Here is the abstract:

In the first quarter of 2022, the Stanford Computational Antitrust project team invited the partnering antitrust agencies to share their advances in implementing computational tools. Here are the results of the survey.

Yoo & Keung on The Political Dynamics of Legislative Reform: Potential Drivers of the Next Communications Statute

Christopher S. Yoo (University of Pennsylvania) and Tiffany Keung (University of Pennsylvania Carey Law School) have posted “The Political Dynamics of Legislative Reform: Potential Drivers of the Next Communications Statute” (Berkeley Technology Law Journal, Forthcoming) on SSRN. Here is the abstract:

Although most studies of major communications reform legislation focus on the merits of their substantive provisions, analyzing the political dynamics that led to the enactment of such legislation can yield important insights. An examination of the tradeoffs that led the major industry segments to support the Telecommunications Act of 1996 provides a useful illustration of the political bargain that it embodies. Application of a similar analysis to the current context identifies seven components that could form the basis for the next communications statute: universal service, pole attachments, privacy, intermediary immunity, net neutrality, spectrum policy, and antitrust reform. Determining how these components might fit together requires an assessment of areas in which industry interests overlap and diverge as well as aspects of the political environment that can make passage of reform legislation more difficult.