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The Challenge that the Advent of Artificial Intelligence (AI) Tools Poses to the Procedures for Determining the Existence of the Preliminary Facts that Condition the Admissibility of Items of Evidence
No full text“[A]d quæstionem facti non respondent judices; . . . ad quæstionem juris not respondent juratores.” Judges do not answer questions of fact; jurors do not answer questions of law.
The jury is a central institution in the American level infrastructure. The Sixth and Seventh Amendments elevate the jury trial right to constitutional status. Of course, when a judge presides at a jury trial, the question that naturally arises is the division of decision-making labor between judge and jury. In the past few decades, the Supreme Court’s Apprendi v. New Jersey line of authority has expanded the jury’s authority to decide facts that come into play during sentencing. The Court has announced that a fact must be decided by a jury if the fact would have the significant impact of increasing the maximum imposable punishment or triggering a higher minimum sentence.
While Apprendi has raised the visibility of the question of the jury’s factfinding power during sentencing, the advent of AI has placed a new focus on the division of labor between judge and jury over the decision of another kind of fact, namely, facts conditioning the admissibility of evidence. The early American courts followed the traditional English view that the judge decides all questions of fact determining the admissibility of evidence. However, later the Jacksonian Democrats presented a convincing critique that the traditional English view enabled trial judges to formally or virtually dictate the jury’s decision.
In the 1920s, to implement that critique leading American scholars developed a new binary procedure for determining preliminary facts. In one category in this procedure, competence, the trial judge follows the English practice. For example, in the case of privileges, the trial judge decides the preliminary facts. The judge listens to the foundational testimony on both sides, passes on credibility, and makes a final decision on the facts. Realistically, it would undermine privilege rules to assign the jury the task of deciding the preliminary facts. It is not that lay jurors are incompetent to decide whether a third party was so close to a client conversing with their attorney that the third party’s presence negated the client’s reasonable expectation of privacy. Rather, the point is that even if the jury were to decide that the evidence is technically inadmissible, there is a grave risk that the jury’s exposure to the evidence would taint their deliberations.
In the second, smaller category, conditional logical relevance, the judge plays only a limited, screening rule and leaves the real decision to the jury. In the case of the preliminary facts of lay witnesses’ personal knowledge and the authenticity of exhibits, it is safe to entrust the decision-making authority to the jury. If the jury decides that the witness “doesn’t know what he’s talking about,” or that the exhibit “isn’t worth the paper it’s written on,” lay common sense will naturally lead the jury to disregard the testimony during the balance of their deliberations.
The scholarly commentary is in agreement that the products of AI tools should not be admitted unless the proponent establishes that the embedded algorithm is reliable. However, the commentary is in conflict on the related issue of whether the judge or jury ought to decide the reliability question. Some commentary refers to Federal Rule of Evidence 104(a) (codifying the competence procedure) while others cite Rules 104(b) and 901(a) (codifying the conditional relevance procedure). Rule 901(b)(9) lists proof of the validity of a “process or system” as one of the preliminary facts subject to Rule 901(a).
This Article argues that for the most part, the question of the reliability of an AI tool should be treated like the question of the validity of run-of-the-mill scientific methodologies governed by Rules 104(a) and 702. While the jury has a simple, binary choice whether or not a witness saw an accident, validation testimony is usually probabilistic in nature. Moreover, while the proponent can often lay a foundation establishing a witness’s personal knowledge or a letter’s authenticity in less than a minute, the foundational testimony for scientific methodology is usually much lengthier, often consuming hundreds of pages of transcript. Finally, while jurors can draw on their fund of lay experience and knowledge to evaluate the personal knowledge and authenticity issues, they ordinarily have to exert much greater mental effort to understand scientific evidence. All three factors make it unrealistic to think that jurors can readily put scientific evidence completely out of mind even if, at a conscious level, they decide that the evidence is technically inadmissible. For that reason, the reliability of an AI tool should be classified as a 104(a) competence issue.
However, the application of Rule 104(a) should not be the end of the judge’s analysis. Some AI tools differ significantly from typical scientific methodologies and instruments in the sense that the tools are more autonomous. Some tools following the unsupervised machine learning model can collect and identify their own training examples. The tools can evolve operational rules that even their original developers neither know of nor fully understand. The tools’ lack of transparency makes it much harder for the jury to intelligently decide how much weight to ascribe to the evidence. Federal Rule of Evidence 403 empowers judges to exclude otherwise admissible evidence when the judge believes that the attendant probative dangers substantially outweigh the probative value of the item of evidence. One of those dangers is that the jury will overestimate the probative worth of the evidence and, for that reason, reach an erroneous verdict. AI tools employing unsupervised machine learning pose that danger to a much greater degree than ordinary scientific methodologies, and for that reason judges should apply Rule 403 more aggressively to such AI tools. Together Rules 104(a), 403, and 703 comprise a sound framework for evaluating the admissibility of proffers of AI evidence
Accommodating Transportation to Work: How Courts Fail to Protect People with Disabilities by Inconsistently Interpreting the ADA’s Requirements to Provide Reasonable Accommodations to Employees
No full textThe Americans with Disabilities Act (ADA) has played a critical role in helping people with disabilities function with greater ease in an inaccessible world. However, it is not perfect and leaves many people without the accommodations they need. One of the many accommodations not directly and consistently protected by the ADA is transportation to and from work. While many Americans have enjoyed the work-from-home boom caused by the pandemic, there are many others that are forced to take remote jobs simply because they cannot secure safe and reliable transportation to and from work due to disability. This significantly narrows their employment opportunities and is discrimination. This comment discusses the creation of the ADA, its impact on Americans, and various circuit courts’ attempts to interpret it in the context of transportation to and from work. It also argues for an amendment to the ADA to clarify that transportation to and from work is related to one’s job and requires reasonable accommodation by the employer, leaving less room for interpretation and accommodations denied by the courts
Partisan Impact? Rejecting the Wisconsin Supreme Court’s New Remedial Redistricting Criterion
No full textIn Clarke v. Wisconsin Elections Commission, the Wisconsin Supreme Court struck down the districting maps for the Wisconsin Legislature that the court had adopted at the close of the Johnson v. Wisconsin Elections Commission trilogy of cases. In so doing, while the Clarke majority based its decision on the maps containing noncontiguous districts, in violation of article IV, sections 4 and 5 of the Wisconsin Constitution, it not-so-subtly introduced a new criterion that would be used to judge remedial maps: “partisan impact.” This Comment critiques the partisan impact criterion through a textualist lens, concluding that the Wisconsin Constitution does not prohibit the Wisconsin Legislature from drawing legislative or congressional maps to benefit a political party. Along the way, this Comment discusses fundamental federal and state redistricting principles, traces the legal and political history underlying the Johnson trilogy and Clarke, and delves into nineteenth-century dictionaries and the debates from Wisconsin’s second constitutional convention. With new lawsuits emerging against Wisconsin’s 2022 congressional map, this Comment urges the Wisconsin Supreme Court to reverse its error in Clarke and to declare partisan gerrymandering a nonjusticiable political question to be properly remedied by the people and their elected representatives
Straight from the Students: The Impact of Law School Experiences on Professional Identity Formation
No full textUnlike other professions, there exists little research about the professional identity formation of law students. This process is key to professional socialization as they transition from student to lawyer. Research from other fields (notably medicine) and limited research on first-year law students suggest that authentic, real-world experiences have a significant impact on professional identity formation. With this hypothesis in mind, this Article presents data gathered from graduating law students about the impact of law school experiences on their ability to think and act like a lawyer. This research was conducted with a goal to aid law schools in directing professional identity formation efforts on the experiences that students identify as most valuable to their professional development
From Beginning to Center Field Spotlight: Celebrating 35 Years of NSLI/CAS Olympic Sport & Arbitration
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Generative AI is Doomed
No full textEric Goldman delivered this talk as the 2024 Nies Lecture at Marquette University School of Law, in Milwaukee, Wisconsin. The talk compares the recent proliferation of generative AI with the Internet’s proliferation in the mid-1990s. In each case, it was clear that the technology would have revolutionary but uncertain impacts on society. However, the public sentiments toward the two innovations have differed radically. The Internet arrived during a period of widespread techno-optimism, creating a regulatory environment that fostered the Internet’s growth. Generative AI, in contrast, has arrived during widespread techno-pessimism and following decades of conditioning about the dangers of “AI.” The difference is consequential: The prevailing regulatory and legal responses to generative AI will limit or even negate its benefits. If society hopes to achieve the full potential of generative AI, we’ll need to adopt a new regulatory approach quickly