Dickinson on The Internet Immunity Escape Hatch

Gregory M. Dickinson (St. Thomas University – School of Law; Stanford Law School) has posted “The Internet Immunity Escape Hatch” (47 BYU L. Rev. 1435 (2022)) on SSRN. Here is the abstract:

Internet immunity doctrine is broken, and Congress is helpless. Under Section 230 of the Communications Decency Act of 1996, online entities are absolutely immune from lawsuits related to content authored by third parties. The law has been essential to the internet’s development over the last twenty years, but it has not kept pace with the times and is now deeply flawed. Democrats demand accountability for online misinformation. Republicans decry politically motivated censorship. And all have come together to criticize Section 230’s protection of bad-actor websites. The law’s defects have put it at the center of public debate, with more than two dozen bills introduced in Congress in the last year alone.

Despite widespread agreement on basic principles, however, legislative action is unlikely. Congress is deadlocked, unable to overcome political polarization and keep pace with technological change. Rather than add to the sizeable literature proposing changes to the law, this Article asks a different question—how to achieve meaningful reform despite a decades-old statute and a Congress unable to act. Even without fresh legislation, reform is possible via an unlikely source: the Section 230 internet immunity statute that is already on the books. Because of its extreme breadth, Section 230 grants significant interpretive authority to the state and federal courts charged with applying the statute. This Article shows how, without any change to the statute, courts could press forward with the very reforms on which Congress has been unable to act.

Unekbas on Competition, Privacy, and Justifications: Invoking Privacy to Justify Abusive Conduct under Article 102 TFEU

Selcukhan Unekbas (European University Institute – Department of Law) has posted “Competition, Privacy, and Justifications: Invoking Privacy to Justify Abusive Conduct under Article 102 TFEU” (Journal of Law, Market & Innovation, forthcoming) on SSRN. Here is the abstract:

This Article aims to delineate the extent to which potentially anticompetitive behavior that simultaneously improve user privacy are cognizable as efficiencies or objective justifications within the context of unilateral conduct cases in European competition law. After mapping the existing literature, it moves on to discuss whether the decisional guidance of the European Commission, as well as the case law of the Union Courts, allow the invocation of privacy as proper grounds to mount a defense against abusive practices. In order to concretize the theoretical discussions, the Article focuses on two recent and highly-relevant developments: Apple’s App-Tracking Transparency initiative, and Google’s unveiling of the Privacy Sandbox. It finds that the state of the law pertaining to the second stage of an abuse case is underdeveloped and is in need of clarification. Nevertheless, considering the recent developments surrounding European competition law in general, and the digital transformation in particular, both efficiencies and objective justifications are likely to find room for application in the digital economy. Whereas efficiencies must be evaluated within the context of substantive symmetry, legal coherence, and economic considerations in a manner that caters to consumer choice, objective justifications may give rise to unintended consequences resulting from judicial and legislative developments. Overall, it is apparent that the case law provides valuable insights as to the implementation of efficiency arguments and objective justifications, but the concepts are nonetheless in need of further analysis vis-à-vis the latest jurisprudence and legislative developments. In that regard, the Article highlights several points of potential contention in the near future.

Mazzini & Scalzo on The Proposal for the Artificial Intelligence Act: Considerations around Some Key Concepts

Gabriele Mazzini (European Commission) and Salvatore Scalzo (same) have posted “The Proposal for the Artificial Intelligence Act: Considerations around Some Key Concepts” on SSRN. Here is the abstract:

The proposal for the Artificial Intelligence (“AI”) Act has broken new ground in many respects. Most visibly, the proposal introduces the first comprehensive draft regulatory framework for AI in the EU and, for the time being, on a global level. In addition, the proposal contains several innovative approaches linked to the specificities of its subject matter and to the fact that it has to interact as smoothly as possible with a very wide range of existing legal frameworks in the EU.

A number of important choices were therefore made to ensure that the AI Act could meet quite unprecedented challenges. The paper aims to briefly outline some of those choices with the hope to help facilitating the understanding of the overall logic of the proposal and it is structured as follows.

After some introductory statements, section II explains the classification of AI systems as products. Section III delves into the essential features of the so-called New Legislative Framework (NLF), a well-known and experimented type of EU legislation that constitutes the fundamental regulatory model of the AI Act. This section also highlights certain adaptations made to the NLF tools in order to take into account certain specificities of AI systems. Having clarified the philosophy behind and the core architecture of the AI Act, section IV discusses briefly how that architecture has been shaped by a number of important points of contact (at times real “interlocks”) between the AI Act and other existing or proposed EU legal acts beyond the realm of NLF product legislation.

Kim on Race-Aware Algorithms: Fairness, Nondiscrimination and Affirmative Action

Pauline Kim (Washington University in St. Louis – School of Law) has posted “Race-Aware Algorithms: Fairness, Nondiscrimination and Affirmative Action” on SSRN. Here is the abstract:

Concerns that predictive algorithms may discriminate are growing, but mitigating or removing bias requires designers to be aware of protected characteristics and take them into account. If they do so, however, will those efforts be considered a form of discrimination? Put concretely, if model builders take race into account to prevent racial bias against Blacks, have they then engaged in discrimination against whites? Some scholars assume so, and seek to justify those practices as valid forms of affirmative action. This Article argues that they have started the analysis in the wrong place. Rather than assuming that disparate treatment has occurred, we should first ask whether race-aware strategies constitute discrimination at all. Despite rhetoric about colorblindness, some forms of race-consciousness are widely accepted as lawful. Because creating an algorithm is a complex, multi-step process involving many choices, tradeoffs and judgment calls, there are many different ways a designer might take race into account, and not all of these strategies entail disparate treatment. Only if a particular strategy is found to be disparate treatment is it necessary to consider whether it is justifiable under affirmative action doctrine. This difference in approach matters, because affirmative action programs bear a heavy legal burden of justification. In addition, treating all race-aware algorithms as a form of disparate treatment reinforces the false notion that leveling the playing field for disadvantaged groups somehow disrupts the entitlements of a previously-advantaged group. It also mistakenly suggests that prior to considering race, algorithms are neutral processes that uncover some objective truth about merit or desert, rather than properly understanding them as human constructs that reflect the choices of their creators.

Bender on Algorithmic Elections

Sarah M.L. Bender (University of Michigan Law School) has posted “Algorithmic Elections” (Michigan Law Review, Forthcoming) on SSRN. Here is the abstract:

Artificial intelligence (AI) has entered election administration. Across the country, election officials are beginning to use AI systems to purge voter records, verify mail-in ballots, and draw district lines. Already, these technologies are having a profound effect on voting rights and democratic processes. However, they have received relatively little attention from AI experts, advocates, and policymakers. Scholars have sounded the alarm on a variety of “algorithmic harms” resulting from AI’s use in the criminal justice system, employment, healthcare, and other civil rights domains. Many of these same algorithmic harms manifest in elections and voting, but have been underexplored and remain unaddressed.

This Note offers three contributions. First, it documents the various forms of “algorithmic decisionmaking” that are currently present in U.S. elections. This is the most comprehensive survey of AI’s use in elections and voting to date. Second, it explains how algorithmic harms resulting from these technologies are disenfranchising eligible voters and disrupting democratic processes. Finally, it identifies several unique characteristics of the U.S. election administration system that are likely to complicate reform efforts and must be addressed to safeguard voting rights.

Jarovsky on Transparency by Design: Reducing Informational Vulnerabilities Through UX Design

Luiza Jarovsky (Tel Aviv University, Buchmann Faculty of Law) has posted “Transparency by Design: Reducing Informational Vulnerabilities Through UX Design” on SSRN. Here is the abstract:

Can transparency help us solve the challenges posed by dark patterns and other unfair practices online? Despite the many weaknesses of transparency obligations in the data protection arena, I suggest that a Transparency by Design (TbD) approach can assist us in better achieving data protection goals, especially by empowering data subjects with accessible information, facilitating the exercise of data protection rights, and helping to reduce informational vulnerabilities. TbD proposes that compliance with transparency rules should happen in all levels of design and user interaction, instead of being restricted to Privacy Policies (PPs) or similar legal statements. In a previous work, I discussed how manipulative design can exploit behavioral biases and generate unfairness; here, I show how failing to support data subjects with accessible information, adequate design and meaningful choices can similarly create an unfair online environment.

This work highlights the shortcomings of transparency rules in the context of the General Data Protection Regulation (GDPR). I demonstrate that, in practice, GDPR obligations do not result in effective transparency for data subjects, increasing unfairness in the data protection context. Consequently, data subjects are most of the time unaware of how, why, and when their data is collected, are uninformed about the risks or broader consequences of their personal data- fueled online activities, do not know their rights regarding their data, and do not have access to meaningful choices.

In order to answer these shortcomings, I propose TbD, so that we – the data subjects – are not only effectively informed of the collection and use of our data, but can also exercise our data subjects’ rights, make meaningful privacy choices, and mitigate our informational vulnerabilities.

The main goal of TbD is that data subjects will be served with information that is meaningful and actionable, instead of a standard block of text that acts as a liability document for the controller’s legal department – as currently happens with PPs. Design, manifested through User Experience (UX), is a central tool in this framework, as it should embed TbD’s values and premises and empower data subjects throughout their interaction with the controller.

Craig Reviewing The Reasonable Robot

Carys J. Craig (Osgoode Hall Law School U Toronto) has posted “The Relational Robot: A Normative Lens for AI Legal Neutrality” (Reviewing Ryan Abbott, The Reasonable Robot, Cambridge University Press, 2020) (Jerusalem Review of Legal Studies (2022 Forthcoming)) on SSRN. Here is the abstract:

In his impactful book, “The Reasonable Robot” (Cambridge UP, 2020), Ryan Abbott proposes a new guiding tenet for the law’s regulation of Artificial Intelligence: AI legal neutrality. This would establish, as a principled starting point, the default position that “the law should not discriminate between AI and human behaviour.” In this Review Essay, I suggest that AI legal neutrality, as conceived by Abbott, is an interesting proposition but a potentially dangerous default principle with which to equip law for the emerging realities of AI. When we scratch beneath the surface, this concept of neutrality or equal treatment is too focused on the individual person/thing, and too reliant on analogical reasoning and false equivalence. It is therefore too far removed from the normative underpinnings of law, its subjects, and its teleology. Instead, I argue, we need a more substantive notion of law’s technological neutrality—one that looks to the law’s normative objectives to set the default. The place to start, I suggest, is not with a legal neutrality that, by design, disregards the inherent differences between humans and robots and their respective behaviors; on the contrary, we need to be fully attentive to the dynamics of human-robot relations in social context – and alert to the dangers of overlooking differences. Rights and responsibilities should be allocated with a clear view to the relationships and subjectivities they shape and the social values they advance.

In Part II, I outline Abbott’s argument in respect of legal neutrality and its application to intellectual property law. I then compare its implications in the patent law sphere to its potential significance in the copyright context. In Part III, I sketch an alternative approach to understanding technological neutrality and law, concerned with consistency in pursuit of normative objectives rather than formal non-discrimination between technologies. I conclude by proposing a relational approach to regulating robots, which would, I believe, bring a much-needed normative lens to the pursuit of legal neutrality.

Schrepel on Law + Technology

Thibault Schrepel (University Paris 1 Panthéon-Sorbonne; VU University Amsterdam; Stanford University’s Codex Center; Sciences Po) has posted “Law + Technology” (Stanford University CodeX Research Paper Series 2022) on SSRN. Here is the abstract:

The classical “law & technology” approach focuses on harms created by technology. This approach seems to be common sense; after all, why be interested—from a legal standpoint—in situations where technology does not cause damage? On close inspection, another approach dubbed “law + technology” can better increase the common good.

The “+” approach builds on complexity science to consider both the issues and positive contributions technology brings to society. The goal is to address the negative ramifications of technology while leveraging its positive regulatory power. Achieving this double objective requires policymakers and regulators to consider a range of intervention methods and choose the ones that are most suitable.

Williams on Algorithmic Price Gouging

Spencer Williams (Golden Gate University School of Law) has posted “Algorithmic Price Gouging” (CUP Research Handbook on Artificial Intelligence & the Law) on SSRN. Here is the abstract:

This chapter examines the intersection of dynamic pricing algorithms and U.S. price gouging laws. Recently, an increasing number of companies, particularly online retailers and technologically- enabled service providers, implemented pricing algorithms that dynamically update prices in real time using data such as competitor prices, market supply quantities, and consumer preferences. While these dynamic pricing algorithms generally increase economic efficiency, they also result in massive price spikes for essential goods and services during emergencies such as the COVID- 19 pandemic. State price gouging laws traditionally regulate price spikes by limiting price increases during emergencies. Drafted largely before the advent of algorithmic pricing and the shift to digital commerce, these laws rely on assumptions such as human-directed pricing and localized markets for goods and services that are inapplicable to companies engaging in algorithmic price gouging. Further, the applicability of state price gouging laws to online retailers selling across state lines remains unclear. The current patchwork of state regulation therefore insufficiently mitigates algorithmic price gouging. The chapter first discusses dynamic pricing algorithms and their relationship to price gouging. The chapter then explores state price gouging laws and identifies key shortfalls with respect to algorithmic price gouging, concluding with the need for a federal solution.

Haber & Harel Ben-Shahar on Algorithmic Parenting

Eldar Haber (University of Haifa – Faculty of Law) and Tammy Harel Ben-Shahar (same) have posted “Algorithmic Parenting” (32 Fordham Intell. Prop. Media & Ent. L.J. 1 (2021)) on SSRN. Here is the abstract:

Growing up in today’s world involves an increasing amount of interaction with technology. The rise in availability, accessibility, and use of the internet, along with social norms that encourage internet connection, make it nearly impossible for children to avoid online engagement. The internet undoubtedly benefits children socially and academically and mastering technological tools at a young age is indispensable for opening doors to valuable opportunities. However, the internet is risky for children in myriad ways. Parents and lawmakers are especially concerned with the tension between important advantages and risks technology bestows on children.

New technological developments in artificial intelligence are beginning to alter the ways parents might choose to safeguard their children from online risks. Recently, emerging AI-based devices and services can automatically detect when a child’s online behavior indicates that their well-being might be compromised or when they are engaging in inappropriate online communication. This technology can notify parents or immediately block harmful content in extreme cases. Referred to as algorithmic parenting in this Article, this new form of parental control has the potential to cheaply and effectively protect children against digital harms. If designed properly, algorithmic parenting would also ensure children’s liberties by neither excessively infringing their privacy nor limiting their freedom of speech and access to information.

This Article offers a balanced solution to the parenting dilemma that allows parents and children to maintain a relationship grounded in trust and respect, while simultaneously providing a safety net in extreme cases of risk. In doing so, it addresses the following questions: What laws should govern platforms with respect to algorithms and data aggregation? Who, if anyone, should be liable when risky behavior goes undetected? Perhaps most fundamentally, relative to the physical world, do parents have a duty to protect their children from online harm? Finally, assuming that algorithmic parenting is a beneficial measure for protecting children from online risks, should legislators and policymakers use laws and regulations to encourage or even mandate the use of such algorithms to protect children? This Article offers a taxonomy of current online threats to children, an examination of the potential shift toward algorithmic parenting, and a regulatory toolkit to guide policymakers in making such a transition.