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FSU Law Focus - 07/30/2021
Get PDFFSU Law Alumni Association Board Takeover: A Message from the President; Student Recruitment Committee Work; Alumni Association Board of Directorshttps://ir.law.fsu.edu/fsu-law-focus/1202/thumbnail.jp
FSU Law Focus - 01/15/2021
Get PDFFrom the Dean: Pave the Way fundraising campaign; FSU Ranked Nation\u27s #3 Law School for Best Quality of Life; Alumni Profile: Michael Ufferman (’97) and Ida Ufferman (’95); Student Profile: 3L Chad Revishttps://ir.law.fsu.edu/fsu-law-focus/1284/thumbnail.jp
FSU Law Focus - 01/22/2021
Get PDFFrom the Dean: Amelia Rea Maguire Business Law Lecture: A Panel on the Economics of Diversity, Equity and Inclusion; Professor Landau’s Scholarship Cited; Alum Profile: Alicia Caridi (’99); Student Profile: 3L Alessandra Norat Mousinhohttps://ir.law.fsu.edu/fsu-law-focus/1293/thumbnail.jp
FSU Law Focus - 09/03/2021
Get PDFFrom the Dean: Fall 2021 Semester back to in-person; Sharpe Joins FSU Law; Alum Profile: Nathan W. Hill (\u2711); Student Profile: 3L Kelly Kaladeenhttps://ir.law.fsu.edu/fsu-law-focus/1096/thumbnail.jp
FSU Law Focus - 04/16/2021
Get PDFFrom the Dean: Student engagement and wellness programming in the age of COVID-19; Spring Law & Business Lecture Series; Alum Profile: Christopher L. Hill (’18); Student Profile: 3L Daniel Corbetthttps://ir.law.fsu.edu/fsu-law-focus/1244/thumbnail.jp
Environmental Law, Disrupted by COVID-19
Get PDFFor over a year, the COVID-19 pandemic and concerns about systemic racial injustice have highlighted the conflicts and opportunities currently faced by environmental law. Scientists uniformly predict that environmental degradation, notably climate change, will cause a rise in diseases, disproportionate suffering among communities already facing discrimination, and significant economic losses. In this Article, members of the Environmental Law Collaborative examine the legal system’s responses to these crises, with the goal of framing opportunities to reimagine environmental law. The Article is excerpted from their book Environmental Law, Disrupted, to be published by ELI Press later this year
Biosupremacy: Big Data, Antitrust, and Monopolistic Power Over Human Behavior
Get PDFSince 2001, five leading technology companies have acquired more than 600 other firms while avoiding antitrust enforcement. By accumulating technologies in adjacent or unrelated industries, these companies have grown so powerful that their influence over human affairs equals that of many governments. Their power stems from data collected by devices that people welcome into their homes, workplaces, schools, and public spaces. When paired with artificial intelligence, these devices form a vast surveillance network that sorts people into increasingly specific categories related to health, sexuality, religion, and other categories. However, this surveillance network was not created solely to observe human behavior; it was also designed to exert control. Accordingly, it is paired with a second network that leverages intelligence gained through surveillance to manipulate people\u27s behavior, nudging them through personalized newsfeeds, targeted advertisements, dark patterns, and other forms of coercive choice architecture. Together, these dual networks of surveillance and control form a global digital panopticon, a modern analog of Bentham\u27s eighteenth-century building designed for total surveillance. Moreover, they enable a pernicious type of influence that Foucault defined as biopower: the ability to measure and modify the behavior of populations to shift social norms. This Article is the first to introduce biopower into antitrust doctrine. It contends that a handful of companies are vying for a dominant share of biopower to achieve biosupremacy, monopolistic power over human behavior. The Article analyzes how companies concentrate biopower through unregulated conglomerate and concentric mergers that add software and devices to their surveillance and control networks. Acquiring technologies in new markets establishes cross-market data flows that send information to acquiring firms across market boundaries. Conglomerate and concentric mergers also expand the control network, establishing beachheads from which platforms exert biopower to shift social norms. Antitrust regulators should expand their conception of consumer welfare to account for the costs imposed by surveillance and coercive choice architecture on product quality. They should revive conglomerate merger control, abandoned in the 1970s, and update it for the Digital Age. Specifically, regulators should halt mergers that concentrate biopower, prohibit the use of dark patterns, and mandate data silos, which contain data within specific markets, to block cross-market data flows
Targeted Transparency as Regulation
Get PDFTraditional government transparency tools are coming under increasing criticism. Laws like the Freedom of Information Act, once thought to revolutionize democracy by opening up government for all to see, have proven to be relatively rough tools (at best) in accomplishing accountability. While the democratic ideals are still celebrated, the increasing costs of broad open-the-government style laws-both monetary and nonmonetary-have not gone unnoticed. Meanwhile, in the regulatory landscape for private companies, targeted disclosure requirements have become increasingly popular methods of encouraging all manner of socially beneficial behavior, be it curbing pollution, making safer consumer products, or ensuring anti-discrimination. Across a wide variety of sectors, companies and businesses now must disclose to the public specific data regarding business finances, environmental risks, safety hazards, and much more. This Article is the first to apply the regulatory disclosure literature to gain insights on government transparency laws, revealing opportunities for designing transparency requirements to more closely hew to accountability goals. We categorize these laws targeted transparency as regulation because though they concern government transparency and not private disclosure, they operate to regulate government actions for specific and measurable accountability goals by incentivizing beneficial, ethical, reasoned conduct by agency officials. Further, our experience with disclosure law provides insights on how to design targeted transparency as regulation requirements, including their promises and limits. While no panacea, targeted transparency as regulation has the potential to play a pivotal role in the next generation of government accountability laws and to provide a partial answer to the critics of broad-based open-the-government style oversight
Algorithmic Fairness, Algorithmic Discrimination
Get PDFThere has been an explosion of concern about the use of computers to make decisions affecting humans, from hiring to lending approvals to setting prison terms. Many have pointed out that using computer programs to make these decisions may result in the propagation of biases or otherwise lead to undesirable outcomes. Many have called for increased transparency and others have called for algorithms to be tuned to produce more racially balanced outcomes. Attention to the problem is likely to grow as computers make increasingly important and sophisticated decisions in our daily lives. Drawing on both the computer science and legal literature on algorithmic fairness, this paper makes four major contributions to the debate over algorithmic discrimination. First, it provides a legal response to a recent flurry of work in computer science seeking to incorporate fairness in algorithmic decision-makers by demonstrating that legal rules generally apply in the form of side constraints, not fairness functions that can be optimized. Second, by looking at the problem through the lens of discrimination law, the paper recognizes that the problems posed by computational decisionmakers closely resemble the historical, institutional discrimination that discrimination law has evolved to control, a response to the claim that this problem is truly novel because it involves computerized decision-making. Third, the paper responds to calls for transparency in computational decision-making by demonstrating how transparency is unnecessary to providing accountability and that discrimination law itself provides a model for how to deal with cases of unfair algorithmic discrimination, with or without transparency. Fourth, the paper addresses a problem that has divided the literature on the topic: how to correct for discriminatory results produced by algorithms. Rather than seeing the problem as a binary one, I offer a third way, one that disaggregates the process of correcting algorithmic decision-makers into two separate decisions: a decision to reject an old process and a separate decision to adopt a new one. Those two decisions are subject to different legal requirements, providing added flexibility to firms and agencies seeking to avoid the worst kinds of discriminatory outcomes. Examples of disparate outcomes generated by algorithms combined with the novelty of computational decision-making are prompting many to push for new regulations to require algorithmic fairness. But, in the end, current discrimination law provides most of the answers for the wide variety of fairness-related claims likely to arise in the context of computational decision-makers, regardless of the specific technology underlying them
Artificially Intelligent Persons
Get PDFArtificial Intelligence (AI) entities seriously challenge traditional legal frameworks for attribution and liability because they operate at an increasing distance from their developers and owners, resulting in accountability gaps. Consider a scenario in which a self-driving car causes injury or even death to a human. Who do we hold accountable? We have no clear answer as to who can be sued or prosecuted because we lack a comprehensive legal understanding of AI entities. Many scholars propose as a solution to the accountability problem attaching liability to the direct source of the harm, the Al entity itself, by first granting it legal personhood. But the law has yet to answer the question of whether AI entities qualify for legal personhood and, if so, on what legal basis. This Article is the first to empirically assess the scope of legal personhood as it relates to AI entities and to answer this question. I make two claims about the problem of legal personhood for AI. First, I argue that the courts\u27 overall approach to legal personhood has been more disparate than many have assumed, and it does not support legal personhood for Al entities. To substantiate this position, I evaluate the legal basis for judicial decisions conferring legal personhood on artificial entities across U.S. courts from 1809 to the present, and I offer a statistical analysis of the frequency with which different conditions for legal personhood appear in these decisions. I find a clear dissonance between legal doctrine and existing theory on legal personhood for AI entities. Second, I argue that empirically understanding the legal landscape for legal personhood prevents courts from conferring legal personhood on AI entities and should give legislators pause before doing so. If courts and legislators consider the conditions for legal personhood that this Article identifies in answering questions of liability for Al entities, they will discover the incompatibility between legal personhood and these entities. Without recognition of this incompatibility, theory, policy, and litigation surrounding Al entities could move in a direction that undermines legal certainty and upsets legal expectation