Law Commons^™

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

Algorithmic Impact Assessments Under The Gdpr: Producing Multi-Layered Explanations, Margot E. Kaminski, Gianclaudio Malgieri

Publications

Policy-makers, scholars, and commentators are increasingly concerned with the risks of using profiling algorithms and automated decision-making. The EU’s General Data Protection Regulation (GDPR) has tried to address these concerns through an array of regulatory tools. As one of us has argued, the GDPR combines individual rights with systemic governance, towards algorithmic accountability. The individual tools are largely geared towards individual “legibility”: making the decision-making system understandable to an individual invoking her rights. The systemic governance tools, instead, focus on bringing expertise and oversight into the system as a whole, and rely on the tactics of “collaborative governance,” that is, …

Legal Opacity: Artificial Intelligence’S Sticky Wicket, Charlotte A. Tschider

Faculty Publications & Other Works

Proponents of artificial intelligence (“AI”) transparency have carefully illustrated the many ways in which transparency may be beneficial to prevent safety and unfairness issues, to promote innovation, and to effectively provide recovery or support due process in lawsuits. However, impediments to transparency goals, described as opacity, or the “black-box” nature of AI, present significant issues for promoting these goals.

An undertheorized perspective on opacity is legal opacity, where competitive, and often discretionary legal choices, coupled with regulatory barriers create opacity. Although legal opacity does not specifically affect AI only, the combination of technical opacity in AI systems with legal opacity …

Ai's Legitimate Interest: Towards A Public Benefit Privacy Model, Charlotte A. Tschider

Faculty Publications & Other Works

Health data uses are on the rise. Increasingly more often, data are used for a variety of operational, diagnostic, and technical uses, as in the Internet of Health Things. Never has quality data been more necessary: large data stores now power the most advanced artificial intelligence applications, applications that may enable early diagnosis of chronic diseases and enable personalized medical treatment. These data, both personally identifiable and de-identified, have the potential to dramatically improve the quality, effectiveness, and safety of artificial intelligence.

Existing privacy laws do not 1) effectively protect the privacy interests of individuals and 2) provide the flexibility …

Law Library Blog (January 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

The Right To Contest Ai, Margot E. Kaminski, Jennifer M. Urban

Publications

Artificial intelligence (AI) is increasingly used to make important decisions, from university admissions selections to loan determinations to the distribution of COVID-19 vaccines. These uses of AI raise a host of concerns about discrimination, accuracy, fairness, and accountability.

In the United States, recent proposals for regulating AI focus largely on ex ante and systemic governance. This Article argues instead—or really, in addition—for an individual right to contest AI decisions, modeled on due process but adapted for the digital age. The European Union, in fact, recognizes such a right, and a growing number of institutions around the world now call for …