The criminal justice system is becoming automated. At every stage, from policing to evidence to parole, AI tools and other technologies guide outcomes. Debates over the pros and cons of these technologies have overlooked a crucial issue: ownership. Developers often claim that details about how their tools work are trade secrets and refuse to disclose that information to criminal defendants or their attorneys. The introduction of intellectual property claims into the criminal justice system raises under-theorised tensions between life, liberty, and property interests. This chapter argues that trade secrets should not be privileged in criminal proceedings. A criminal trade secret privilege is ahistorical, harmful to defendants, and unnecessary to protect the interests of the secret holder. Meanwhile, compared to substantive trade secret law, the privilege overprotects intellectual property. Further, privileging trade secrets in criminal proceedings fails to serve the theoretical purpose of either trade secret law or privilege law. The trade secret inquiry sheds new light on how evidence rules do, and should, function differently in civil and criminal cases.

New health care devices, including at-home diagnostic devices, are generating and aggregating data on patients’ health at a staggering pace. Yet much of that data is inaccessible because it is held in data siloes, most often cloud services controlled by device manufacturers. This proprietary siloing of patient data is problematic from ethical, economic, scientific, and broad public policy perspectives. This chapter frames these concerns and begins to sketch a regulatory framework for patient access to health care device data. As with other consumer data, breaking down siloes and securing patients’ access to their device data safeguards patients’ ownership interests, promotes patients’ ability to maintain and repair their equipment, and encourages interoperability and competition. Yet, data access is especially important for health data: It allows patients to make informed decisions about their own care, and it enables motivated citizen-scientists to study their own conditions and innovate in response to them. Patient access to device data may also be a first step toward building publicly accessible, responsibly governed datasets of so-called “real-world evidence” – which are increasingly essential to validate the accuracy and reliability of current diagnostic devices – and to invent and validate future devices, drugs, and other precision medicine interventions. These interests motivate the development of our proposed framework. Drawing from related experiences with clinical trial data and electronic health records, this chapter identifies the key considerations for a framework that protects key interests, such as privacy and data security, while unlocking the benefits of broader data sharing.

Algorithms in society are both innocuous and ubiquitous. They seamlessly permeate both our on- and offline lives, quietly distilling the volumes of data each of us now creates. Today, algorithms determine the optimal way to produce and ship goods, the prices we pay for those goods, the money we can borrow, the people who teach our children, and the books and articles we read – reducing each activity to an actuarial risk or score. “If every algorithm suddenly stopped working,” Pedro Domingos hypothesized, “it would be the end of the world as we know it.”1