Law Commons^™

Naïve Realism, Cognitive Bias, And The Benefits And Risks Of Ai, Harry Surden

Publications

In this short piece I comment on Orly Lobel's book on artificial intelligence (AI) and society "The Equality Machine." Here, I reflect on the complex topic of aI and its impact on society, and the importance of acknowledging both its positive and negative aspects. More broadly, I discuss the various cognitive biases, such as naïve realism, epistemic bubbles, negativity bias, extremity bias, and the availability heuristic, that influence individuals' perceptions of AI, often leading to polarized viewpoints. Technology can both exacerbate and ameliorate these biases, and I commend Lobel's balanced approach to AI analysis as an example to emulate.

Although …

Administrative Law In The Automated State, Cary Coglianese

All Faculty Scholarship

In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …

Of Monopolies And Monocultures: The Intersection Of Patents And National Security, Charles Duan

Articles in Law Reviews & Other Academic Journals

It was certainly an odd thing for the Department of Justice attorney arguing for the United States to appear before the Ninth Circuit to tell the appellate judges that a federal agency was wrong. This was what happened in a Federal Trade Commission enforcement action against Qualcomm Inc., a semiconductor technology company. As a substantial holder of patents on mobile communications technologies and also a leading manufacturer of chips used in that same industry, the FTC charged Qualcomm with anticompetitive conduct; the district court agreed and enjoined Qualcomm from certain patent licensing practices. It was that award of injunctive relief …

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 …

Ethical Testing In The Real World: Evaluating Physical Testing Of Adversarial Machine Learning, Kendra Albert, Maggie Delano, Jonathon Penney, Afsaneh Ragot, Ram Shankar Siva Kumar

Articles, Book Chapters, & Popular Press

This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as “real world.” Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing …

"Cyborg Justice" And The Risk Of Technological-Legal Lock-In, Rebecca Crootof

Law Faculty Publications

Although Artificial Intelligence (AI) is already of use to litigants and legal practitioners, we must be cautious and deliberate in incorporating AI into the common law judicial process. Human beings and machine systems process information and reach conclusions in fundamentally different ways, with AI being particularly ill-suited for the rule application and value balancing required of human judges. Nor will “cyborg justice”—hybrid human/AI judicial systems that attempt to marry the best of human and machine decisionmaking and minimize the drawbacks of both—be a panacea. While such systems would ideally maximize the strengths of human and machine intelligence, they might also …

Lessons From Literal Crashes For Code, Margot Kaminski

Publications

No abstract provided.

Emerging Technologies Challenging Current Legal Paradigms, W. Keith Robinson, Joshua T. Smith

Faculty Journal Articles and Book Chapters

U.S. patent law has made assumptions about where new inventions will be created, who will create them, and how they will be infringed. Throughout history, emerging technologies have challenged these paradigms. This decade’s emerging technologies will allow humans to create in virtual worlds, connect billions of every day devices via the Internet, and use artificial intelligence to invent across technology fields. If countries like the U.S. wish to encourage inventors to seek patent protection in these emerging areas, then a paradigm shift in the law must occur. Specifically, the law must clarify patent eligibility, recognize the increasing role of artificial …

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 …

From Google To Tolstoy Bot: Should The First Amendment Protect Speech Generated By Algorithms?, Margot Kaminski

Publications

No abstract provided.