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Volume 19 - Issue 1



Grimm, Paul W.,Grossman, Maura R.,Cormack, Gordon V. | December 1, 2021

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal profession. The article underscores the importance of determining the validity of an AI application (i.e., how accurately the AI measures, classifies, or predicts what it is designed to), as well as its reliability (i.e., the consistency with which the AI produces accurate results when applied to the same or substantially similar circumstances), in deciding whether it should be admitted into evidence in civil and criminal cases. The article further discusses factors that can affect the validity and reliability of AI evidence, including bias of various types, “function creep,” lack of transparency and explainability, and the sufficiency of the objective testing of AI applications before they are released for public use. The article next provides an in-depth discussion of the evidentiary principles that govern whether AI evidence should be admitted in court cases, a topic which, at present, is not the subject of comprehensive analysis in decisional law. The focus of this discussion is on providing a step-by-step analysis of the most important issues, and the factors that affect decisions on whether to admit AI evidence. Finally, the article concludes with a discussion of practical suggestions intended to assist lawyers and judges as they are called upon to introduce, object to, or decide on whether to admit AI evidence.


Ma, Megan,Wilson, Bryan | December 1, 2021

Legal interpretation is a linguistic venture. In judicial opinions, for example, courts are often asked to interpret the text of statutes and legislation. As time has shown, this is not always as easy as it sounds. Matters can hinge on vague or inconsistent language and, under the surface, human biases can impact the decision-making of judges. This raises an important question: what if there was a method of extracting the meaning of statutes consistently? That is, what if it were possible to use machines to encode legislation in a mathematically precise form that would permit clearer responses to legal questions? This article attempts to unpack the notion of machine-readability, providing an overview of both its historical and recent developments. The paper will reflect on logic syntax and symbolic language to assess the capacity and limits of representing legal knowledge. In doing so, the paper seeks to move beyond existing literature to discuss the implications of various approaches to machine-readable legislation. Importantly, this study hopes to highlight the challenges encountered in this burgeoning ecosystem of machine-readable legislation against existing human-readable counterparts.



Kovach, Ben | December 1, 2021

As artificial intelligence (AI) system’s capabilities advance, the law has struggled to keep pace. Nowhere is this more evident than patent law’s refusal to recognize AI as an inventor. This is precisely what happened when, in 2020, the U.S. Patent and Trademark Office (USPTO) ruled that it will not accept an AI system as a named inventor on a patent. This note explores untenable legal fiction that the USPTO’s ruling has created. First, it explores the current state of AI systems, focusing on those capable of invention. Next, it examines patent law’s inventorship doctrine and the USPTO’s application of that doctrine to AI inventors. The note then explains that disallowing AI systems as inventors does not map well onto patent law’s most common justifications. Finally, the note recommends a solution that maximizes patent law’s incentive structure; AI systems should be allowed as named inventors when patent ownership has been pre-contracted away to a natural person. If patent ownership has not been pre-contracted, the idea should enter the public domain and be unpatentable.