Law Commons^™

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; …

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 …

Content Moderation As Surveillance, Hannah Bloch-Wehba

Faculty Scholarship

Technology platforms are the new governments, and content moderation is the new law, or so goes a common refrain. As platforms increasingly turn toward new, automated mechanisms of enforcing their rules, the apparent power of the private sector seems only to grow. Yet beneath the surface lies a web of complex relationships between public and private authorities that call into question whether platforms truly possess such unilateral power. Law enforcement and police are exerting influence over platform content rules, giving governments a louder voice in supposedly “private” decisions. At the same time, law enforcement avails itself of the affordances of …

Data Privacy, Human Rights, And Algorithmic Opacity, Sylvia Lu

Fellow, Adjunct, Lecturer, and Research Scholar Works

Decades ago, it was difficult to imagine a reality in which artificial intelligence (AI) could penetrate every corner of our lives to monitor our innermost selves for commercial interests. Within just a few decades, the private sector has seen a wild proliferation of AI systems, many of which are more powerful and penetrating than anticipated. In many cases, AI systems have become “the power behind the throne,” tracking user activities and making fateful decisions through predictive analysis of personal information. Despite the growing power of AI, proprietary algorithmic systems can be technically complex, legally claimed as trade secrets, and managerially …

Problematic Interactions Between Ai And Health Privacy, W. Nicholson Price Ii

Articles

Problematic Interactions Between AI and Health Privacy Nicholson Price, University of Michigan Law SchoolFollow Abstract The interaction of artificial intelligence (AI) and health privacy is a two-way street. Both directions are problematic. This Essay makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set …

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 …

Submission To The Toronto Police Services Board’S Use Of New Artificial Intelligence Technologies Policy- Leaf And The Citizen Lab, Suzie Dunn, Kristen Mj Thomasen, Kate Robertson, Pam Hrick, Cynthia Khoo, Rosel Kim, Ngozi Okidegbe, Christopher Parsons

Reports & Public Policy Documents

We write as a group of experts in the legal regulation of artificial intelligence (AI), technology-facilitated violence, equality, and the use of AI systems by law enforcement in Canada. We have experience working within academia and legal practice, and are affiliated with LEAF and the Citizen Lab who support this letter.

We reviewed the Toronto Police Services Board Use of New Artificial Intelligence Technologies Policy and provide comments and recommendations focused on the following key observations:

1. Police use of AI technologies must not be seen as inevitable
2. A commitment to protecting equality and human rights must be integrated …

Contracting For Algorithmic Accountability, Cary Coglianese, Erik Lampmann

All Faculty Scholarship

As local, state, and federal governments increase their reliance on artificial intelligence (AI) decision-making tools designed and operated by private contractors, so too do public concerns increase over the accountability and transparency of such AI tools. But current calls to respond to these concerns by banning governments from using AI will only deny society the benefits that prudent use of such technology can provide. In this Article, we argue that government agencies should pursue a more nuanced and effective approach to governing the governmental use of AI by structuring their procurement contracts for AI tools and services in ways that …

Law Enforcement’S Use Of Facial Recognition Software In United States Cities, Samantha Jean Wunschel

Honors Program Theses and Projects

Facial recognition software is something we use every day, whether it’s a suggested tag on our Facebook post or a faster way to unlock our phones. As technology becomes increasingly pervasive in our lives, law enforcement has adapted to utilize the new tools available in accessory to their investigations and the legal process.

The Law Of Black Mirror - Syllabus, Yafit Lev-Aretz, Nizan Packin

Open Educational Resources

Using episodes from the show Black Mirror as a study tool - a show that features tales that explore techno-paranoia - the course analyzes legal and policy considerations of futuristic or hypothetical case studies. The case studies tap into the collective unease about the modern world and bring up a variety of fascinating key philosophical, legal, and economic-based questions.

Ethics, Ai, Mass Data And Pandemic Challenges: Responsible Data Use And Infrastructure Application For Surveillance And Pre-Emptive Tracing Post-Crisis, Mark Findlay, Jia Yuan Loke, Nydia Remolina Leon, Yum Yin, Benjamin (Tan Renyan) Tham

Research Collection Yong Pung How School Of Law

As the COVID-19 health pandemic rages governments and private companies across the globe are utilising AI-assisted surveillance, reporting, mapping and tracing technologies with the intention of slowing the spread of the virus. These technologies have the capacity to amass personal data and share for community control and citizen safety motivations that empower state agencies and inveigle citizen co-operation which could only be imagined outside such times of real and present danger. While not cavilling with the short-term necessity for these technologies and the data they control, process and share in the health regulation mission, this paper argues that this infrastructure …

Gdpr And The Importance Of Data To Ai Startups, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans

Faculty Scholarship

What is the impact of the European Union’s General Data Protection Regime (“GDPR”) and data regulation on AI startups? How important is data to AI product development? We study these questions using unique survey data of commercial AI startups. AI startups rely on data for their product development. Given the scale and scope of their business models, these startups are particularly susceptible to policy changes impacting data collection, storage and use. We find that training data and frequent model refreshes are particularly important for AI startups that rely on neural nets and ensemble learning algorithms. We also find that firms …

Automation In Moderation, Hannah Bloch-Wehba

Faculty Scholarship

This Article assesses recent efforts to encourage online platforms to use automated means to prevent the dissemination of unlawful online content before it is ever seen or distributed. As lawmakers in Europe and around the world closely scrutinize platforms’ “content moderation” practices, automation and artificial intelligence appear increasingly attractive options for ridding the Internet of many kinds of harmful online content, including defamation, copyright infringement, and terrorist speech. Proponents of these initiatives suggest that requiring platforms to screen user content using automation will promote healthier online discourse and will aid efforts to limit Big Tech’s power.

In fact, however, 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 …

The Healthcare Privacy-Artificial Intelligence Impasse, Charlotte A. Tschider

Faculty Publications & Other Works

With the advent of the Internet, wireless technologies, advanced computing, and, ultimately, the integration of mobile devices into patient care, medical device technologies have revolutionized the healthcare sector. What once was a highly personal, one-to-one relationship between physician and patient has now been expanded, including medical device manufacturers, third party healthcare system providers, even physician-as-a-service for interpreting the data complex systems churn out. The introduction of technology to the healthcare field has, at an ever-increasing rate, transformed human health management.

Reworking privacy commitments in an AI world is an important endeavor. It may mean that we reconceptualize what these rights …

Open Banking: Regulatory Challenges For A New Form Of Financial Intermediation In A Data-Driven World, Nydia Remolina

Centre for AI & Data Governance

Data has taken immense importance in the last years. Consider the amount of data that is being collected worldwide every day, industries are reshaping their activities into a data-driven business. The digital transformation of all industries, portent of the fourth industrial revolution, is creating a new kind of economy based on the datafication of almost any aspect of human social, political and economic activity as a result of the information generated by the numerous daily routines of digitally connected individuals and technology. The financial services industry is part of this trend. Embracing the digital revolution and creating the right foundations …

Power, Process, And Automated Decision-Making, Ari Ezra Waldman

Articles & Chapters

Many decisions that used to be made by humans are now made by machines. And yet, automated decision-making systems based on “big data” – powered algorithms and machine learning are just as prone to mistakes, biases, and arbitrariness as their human counterparts. The result is a technologically driven decision-making process that seems to defy interrogation, analysis, and accountability and, therefore, undermines due process. This should make algorithmic decision-making an illegitimate source of authority in a liberal democracy. This Essay argues that algorithmic decision-making is a product of the neoliberal project to undermine social values like equality, nondiscrimination, and human flourishing …

Humans Forget, Machines Remember: Artificial Intelligence And The Right To Be Forgotten, Tiffany Li, Eduard Fosch Villaronga, Peter Kieseberg

Faculty Scholarship

To understand the Right to be Forgotten in context of artificial intelligence, it is necessary to first delve into an overview of the concepts of human and AI memory and forgetting. Our current law appears to treat human and machine memory alike – supporting a fictitious understanding of memory and forgetting that does not comport with reality. (Some authors have already highlighted the concerns on the perfect remembering.) This Article will examine the problem of AI memory and the Right to be Forgotten, using this example as a model for understanding the failures of current privacy law to reflect the …

Expanding The Artificial Intelligence-Data Protection Debate, Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

Robotic Speakers And Human Listeners, Helen Norton

Publications

In their new book, Robotica, Ron Collins and David Skover assert that we protect speech not so much because of its value to speakers but instead because of its affirmative value to listeners. If we assume that the First Amendment is largely, if not entirely, about serving listeners’ interests—in other words, that it’s listeners all the way down—what would a listener-centered approach to robotic speech require? This short symposium essay briefly discusses the complicated and sometimes even dark side of robotic speech from a listener-centered perspective.

The Scored Society: Due Process For Automated Predictions, Danielle Keats Citron, Frank A. Pasquale

Faculty Scholarship

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data.

Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform …