Law Commons^™

How Can I Tell If My Algorithm Was Reasonable?, Karni A. Chagal-Feferkorn

Michigan Technology Law Review

Self-learning algorithms are gradually dominating more and more aspects of our lives. They do so by performing tasks and reaching decisions that were once reserved exclusively for human beings. And not only that—in certain contexts, their decision-making performance is shown to be superior to that of humans. However, as superior as they may be, self-learning algorithms (also referred to as artificial intelligence (AI) systems, “smart robots,” or “autonomous machines”) can still cause damage.

When determining the liability of a human tortfeasor causing damage, the applicable legal framework is generally that of negligence. To be found negligent, the tortfeasor must have …

Clearing Opacity Through Machine Learning, W. Nicholson Price Ii, Arti K. Rai

Articles

Artificial intelligence and machine learning represent powerful tools in many fields, ranging from criminal justice to human biology to climate change. Part of the power of these tools arises from their ability to make predictions and glean useful information about complex real-world systems without the need to understand the workings of those systems.

Equal Protection Under Algorithms: A New Statistical And Legal Framework, Crystal S. Yang, Will Dobbie

Michigan Law Review

In this Article, we provide a new statistical and legal framework to understand the legality and fairness of predictive algorithms under the Equal Protection Clause. We begin by reviewing the main legal concerns regarding the use of protected characteristics such as race and the correlates of protected characteristics such as criminal history. The use of race and nonrace correlates in predictive algorithms generates direct and proxy effects of race, respectively, that can lead to racial disparities that many view as unwarranted and discriminatory. These effects have led to the mainstream legal consensus that the use of race and nonrace correlates …

Title 2.0: Discrimination Law In A Data-Driven Society, Bryan Casey

Journal of Law and Mobility

More than a quarter century after civil rights activists pioneered America’s first ridesharing network, the connections between transportation, innovation, and discrimination are again on full display. Industry leaders such as Uber, Amazon, and Waze have garnered widespread acclaim for successfully combatting stubbornly persistent barriers to transportation. But alongside this well-deserved praise has come a new set of concerns. Indeed, a growing number of studies have uncovered troubling racial disparities in wait times, ride cancellation rates, and service availability in companies including Uber, Lyft, Task Rabbit, Grubhub, and Amazon Delivery.

Surveying the methodologies employed by these studies reveals a subtle, but …

Robot Criminals, Ying Hu

University of Michigan Journal of Law Reform

When a robot harms humans, are there any grounds for holding it criminally liable for its misconduct? Yes, provided that the robot is capable of making, acting on, and communicating the reasons behind its moral decisions. If such a robot fails to observe the minimum moral standards that society requires of it, labeling it as a criminal can effectively fulfill criminal law’s function of censuring wrongful conduct and alleviating the emotional harm that may be inflicted on human victims.

Imposing criminal liability on robots does not absolve robot manufacturers, trainers, or owners of their individual criminal liability. The former is …

Medical Malpractice And Black-Box Medicine, W. Nicholson Price Ii

Book Chapters

The explosive proliferation of health data has combined with the rapid development of machine-learning algorithms to enable a new form of medicine: “black-box medicine.” In this phenomenon, algorithms troll through tremendous databases of health data to find patterns that can be used to guide care, whether by predicting unknown patient risks, selecting the right drug, suggesting a new use of an old drug, or triaging patients to preserve health resources. These decisions differ from previous data-based decisions because black-box medicine is, by its nature, opaque; that is, the bases for black-box decisions are unknown and unknowable.

Black-box medicine raises a …

Regulating Black-Box Medicine, W. Nicholson Price Ii

Michigan Law Review

Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they …

Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii

Articles

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques—terms I will explain momentarily—innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from healthcare data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including …

A Comment On Privacy And Accountability In Black-Box Medicine, Carl E. Schneider

Michigan Telecommunications & Technology Law Review

Human institutions and activities cannot avoid failures. Anxiety about them often provokes governments to try to prevent those failures. When that anxiety is vivid and urgent, government may do so without carefully asking whether regulation’s costs justify their benefits. Privacy and Accountability in Black Box Medicine admirably labors to bring discipline and rationality to thinking about an important development — the rise of “black-box medicine” — before it causes injuries regulation should have prevented and before it is impaired by improvident regulation. That is, Privacy and Accountability weighs the costs against the benefits of various forms of regulation across the …

The Racist Algorithm?, Anupam Chander

Michigan Law Review

Review of The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale.

Tightening The Ooda Loop: Police Militarization, Race, And Algorithmic Surveillance, Jeffrey L. Vagle

Michigan Journal of Race and Law

This Article examines how military automated surveillance and intelligence systems and techniques, when used by civilian police departments to enhance predictive policing programs, have reinforced racial bias in policing. I will focus on two facets of this problem. First, I investigate the role played by advanced military technologies and methods within civilian police departments. These approaches have enabled a new focus on deterrence and crime prevention by creating a system of structural surveillance where decision support relies increasingly upon algorithms and automated data analysis tools and automates de facto penalization and containment based on race. Second, I will explore these …

Big Data, Patents, And The Future Of Medicine, W. Nicholson Price Ii

Articles

Big data has tremendous potential to improve health care. Unfortunately, intellectual property law isn’t ready to support that leap. In the next wave of data- driven medicine, black-box medicine, researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. Black-box medicine offers potentially immense benefits, but also requires substantial high investment. Firms must develop new datasets, models, and validations, which are all nonrivalrous information goods with significant spillovers, requiring incentives for welfare-optimizing investment. Current intellectual property law fails to provide adequate incentives for black- box medicine. The Supreme Court …

Privacy And Accountability In Black-Box Medicine, Roger Allan Ford, W. Nicholson Price Ii

Michigan Telecommunications & Technology Law Review

Black-box medicine—the use of big data and sophisticated machine-learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, …

Face-To-Face With Facial Recognition Evidence: Admissibility Under The Post-Crawford Confrontation Clause, Joseph Clarke Celentino

Michigan Law Review

In Crawford v. Washington, the Supreme Court announced a major change in Confrontation Clause doctrine, abandoning a decades-old framework that focused on the common law principles of hearsay analysis: necessity and reliability. The new doctrine, grounded in an originalist interpretation of the Sixth Amendment, requires courts to determine whether a particular statement is testimonial. But the Court has struggled to present a coherent definition of the term testimonial. In its subsequent decisions, the Court illustrated that its new Confrontation Clause doctrine could be used to bar forensic evidence, including laboratory test results, if the government failed to produce the …

Taking A Byte Out Of Abusive Agency Discretion: A Proposal For Disclosure In The Use Of Computer Models, John P. Barker

University of Michigan Journal of Law Reform

This Note examines the need for comprehensive requirements for the release of information pertaining to the use of computer-generated simulations used by federal administrative agencies or parties appearing before regulatory bodies. Part I of this Note defines computer models, identifies some of their current uses in administrative proceedings and describes the advantages of these models. Part II reviews the current requirements for documentation of computer models and the judicial review standards for agency findings. Part III examines the potential problems in the use of models and discusses the need for more adequate disclosure. Part IV describes several tests for verifying …