Law Commons^™

The Present And Future Of Ai Usage In The Banking And Financial Decision-Making Processes Within The Developing Indian Economy, Dr. Shouvik Kumar Guha, Bash Savage-Mansary, Dr. Navyajyoti Samanta

Indian Journal of Law and Technology

In course of this paper, the authors have soght to examine the extent to which technology based on artificial intelligence (AI) have made inroads into the banking and financial sectors of a developing economy like India. The paper begins with providing a contextual background to the adoption of such technology in the global financial arena. It then proceeds to identify and categorise the forms of AI currently being used in the Indian financial sector and also considers the different channels of operation where such technology is in vogue. The advantages of using such technology and the future goals for integrating …

National Telecommunications And Information Administration: Comments From Researchers At Boston University And The University Of Chicago, Ran Canetti, Aloni Cohen, Chris Conley, Mark Crovella, Stacey Dogan, Marco Gaboardi, Woodrow Hartzog, Rory Van Loo, Christopher Robertson, Katharine B. Silbaugh

Faculty Scholarship

These comments were composed by an interdisciplinary group of legal, computer science, and data science faculty and researchers at Boston University and the University of Chicago. This group collaborates on research projects that grapple with the legal, policy, and ethical implications of the use of algorithms and digital innovation in general, and more specifically regarding the use of online platforms, machine learning algorithms for classification, prediction, and decision making, and generative AI. Specific areas of expertise include the functionality and impact of recommendation systems; the development of Privacy Enhancing Technologies (PETs) and their relationship to privacy and data security laws; …

Aclu V. Clearview Ai, Inc.,, Isra Ahmed

DePaul Journal of Art, Technology & Intellectual Property Law

No abstract provided.

Copyright Throughout A Creative Ai Pipeline, Sancho Mccann

Canadian Journal of Law and Technology

Consider the following fact pattern.

Alex paints some original works on canvas and posts photos of them online. Becca downloads those images and uses them to train an AI (training configures the AI’s model parameters to useful values). Becca posts the resulting trained parameter values on her website under a license that reserves to Becca the right to use the parameters commercially. Cory uses those parameter values in a program that is designed to produce artwork. Cory clicks create and the program produces a work. This work is new to Cory, but it looks a lot like one of Alex’s …

Regulating The Risks Of Ai, Margot E. Kaminski

Publications

Companies and governments now use Artificial Intelligence (“AI”) in a wide range of settings. But using AI leads to well-known risks that arguably present challenges for a traditional liability model. It is thus unsurprising that lawmakers in both the United States and the European Union (“EU”) have turned to the tools of risk regulation in governing AI systems.

This Article describes the growing convergence around risk regulation in AI governance. It then addresses the question: what does it mean to use risk regulation to govern AI systems? The primary contribution of this Article is to offer an analytic framework for …

Gauging The Acceptance Of Contact Tracing Technology: An Empirical Study Of Singapore Residents’ Concerns With Sharing Their Information And Willingness To Trust, Ee-Ing Ong, Wee Ling Loo

Research Collection Yong Pung How School Of Law

In response to the COVID-19 pandemic, governments began implementing various forms of contact tracing technology. Singapore’s implementation of its contact tracing technology, TraceTogether, however, was met with significant concern by its population, with regard to privacy and data security. This concern did not fit with the general perception that Singaporeans have a high level of trust in its government. We explore this disconnect, using responses to our survey (conducted pre-COVID-19) in which we asked participants about their level of concern with the government and business collecting certain categories of personal data. The results show that respondents had less concern with …

Artificial Intelligence In Canadian Healthcare: Will The Law Protect Us From Algorithmic Bias Resulting In Discrimination?, Bradley Henderson, Colleen M. Flood, Teresa Scassa

Canadian Journal of Law and Technology

In this article, we canvas why AI may perpetuate or exacerbate extant discrimination through a review of the training, development, and implementation of healthcare-related AI applications and set out policy options to militate against such discrimination. The article is divided into eight short parts including this introduction. Part II focuses on explaining AI, some of its basic functions and processes, and its relevance to healthcare. In Part III, we define and explain the difference and relationship between algorithmic bias and data bias, both of which can result in discrimination in healthcare settings, and provide some prominent examples of healthcare-related AI …

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 …

Privacy In Pandemic: Law, Technology, And Public Health In The Covid-19 Crisis, Tiffany Li

Faculty Scholarship

The COVID-19 pandemic has caused millions of deaths and disastrous consequences around the world, with lasting repercussions for every field of law, including privacy and technology. The unique characteristics of this pandemic have precipitated an increase in use of new technologies, including remote communications platforms, healthcare robots, and medical AI. Public and private actors are using new technologies, like heat sensing, and technologically-influenced programs, like contact tracing, alike in response, leading to a rise in government and corporate surveillance in sectors like healthcare, employment, education, and commerce. Advocates have raised the alarm for privacy and civil liberties violations, but the …

Regulation Of Algorithmic Tools In The United States, Christopher S. Yoo, Alicia Lai

All Faculty Scholarship

Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search …

Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert

Articles, Book Chapters, & Popular Press

Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US and EU are likewise putting security and safety of AI systems as a top priority.

Now, research on adversarial …

Politics Of Adversarial Machine Learning, Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar

Articles, Book Chapters, & Popular Press

In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference …

A New Frontier Facing Attorneys And Paralegals: The Promise & Challenges Of Artificial Intelligence As Applied To Law & Legal Decision-Making, Marissa Moran

Publications and Research

Artificial Intelligence/AI invisibly navigates and informs our lives today and may also be used to determine a client’s legal fate. Through executive order, statements by a U.S. Supreme Court justice and a Congressional Commission on AI, all three branches of the United States government have addressed the use of AI to resolve societal and legal matters. Pursuant to the American Bar Association Model Rules of Professional Conduct[i] and New York Rules of Professional Conduct (NYRPC), [ii] the legal profession recognizes the need for competency in technology which requires both substantive knowledge of law and competent use of technology for …

The Right To Explanation, Explained, Margot E. Kaminski

Publications

Many have called for algorithmic accountability: laws governing decision-making by complex algorithms, or AI. The EU’s General Data Protection Regulation (GDPR) now establishes exactly this. The recent debate over the right to explanation (a right to information about individual decisions made by algorithms) has obscured the significant algorithmic accountability regime established by the GDPR. The GDPR’s provisions on algorithmic accountability, which include a right to explanation, have the potential to be broader, stronger, and deeper than the preceding requirements of the Data Protection Directive. This Essay clarifies, largely for a U.S. audience, what the GDPR actually requires, incorporating recently released …

Information And The Regulatory Landscape: A Growing Need To Reconsider Existing Legal Frameworks, Anjanette H. Raymond

Washington and Lee Journal of Civil Rights and Social Justice

No abstract provided.

Substantiating Big Data In Health Care, Nathan Cortez

Faculty Journal Articles and Book Chapters

Predictive analytics and "big data" are emerging as important new tools for diagnosing and treating patients. But as data collection becomes more pervasive, and as machine learning and analytical methods become more sophisticated, the companies that traffic in health-related big data will face competitive pressures to make more aggressive claims regarding what their programs can predict. Already, patients, practitioners, and payors are inundated with claims that software programs, "apps," and other forms of predictive analytics can help solve some of the health care system's most pressing problems. This article considers the evidence and substantiation that we should require of these …