Myriad examples—from the rise of chief technology officers in law firms to the over $1.6 trillion invested in legal tech start-ups in 2018 to the use of AI-assisted drafting tools by Walmart’s legal department—demonstrate how technology is inexorably changing the American legal profession and courts, despite their small “c” conservative nature. When Chief Justice John Roberts was asked whether “smart machines, driven with artificial intelligences, will assist with courtroom fact finding or, more controversially even, judicial decision making,” he replied, “It’s a day that’s here….” And the legal community’s integration of more mundane—if no less important—technological tools such as videoconferencing into its existing practices has been dramatically hastened by the COVID-19 pandemic. A prominent example of this trend was when the Supreme Court of the United States held its first telephonic hearing in May.
While legal scholars and the popular press frequently comment on these developments, even the most expansive futurist takes about robot judging focus on how lawyers and the judiciary are (or will be) using new technology to do their traditional work. And courts mostly wrestle with how to adapt existing doctrine to specific applications of new tools. Against this context, David Freeman Engstrom and Jonah Gelbach’s Legal Tech, Civil Procedure, and the Future of Adversarialism marks a significant theoretical push forward, identifying and exploring the overarching question of how legal tech and the civil justice system’s procedural rules mutually shape each other.
In sketching their answers to this question, Engstrom and Gelbach provide two key insights about simultaneous interactions that will guide future research and reform efforts in the near-to-medium term. First, they explain how civil procedure acts on legal technology by shaping the incentives for innovation through the rules governing its use in litigation. Second, they show how legal technology acts on civil procedure doctrine by altering two foundational concerns—the reduction of high and asymmetric litigation costs and the widening of information asymmetries.
Engstrom and Gelbach begin by surveying existing legal technology, presenting a robust account of different tasks, along with the end user, litigation stage, necessary legal or technical expertise, data inputs, and product examples for each task. They also assess the likely technical trajectory of legal technology and the movement from tools that handle routinized tasks like e-discovery or digital reference retrieval to more sophisticated tools that use natural language processing and other machine learning techniques to make case predictions or draft legal documents. Engstrom and Gelbach then discuss regulatory, cultural, and technical problems that might limit the growth of legal tech. Of these, the technical barriers loom largest—whether difficulties generic to natural-language processing or law-specific difficulties like the dynamism of law and data deficiencies due to confidential settlements.
Notwithstanding a cautious note about how fast and far legal technology will advance in the near-to-medium term, Engstrom and Gelbach identify several important implications of the emerging academic literature on the topic. As to the legal profession, legal tech might result in lawyer de-skilling and de-centering, as non-lawyer professionals are brought in to manage advanced technological tools. They question whether the rule of law will suffer as lawyers are displaced by technologists whose expertise is shaped without the traditional norms of the legal profession. Additionally, they considerhow legal technology tools might impact the law, cautioning against a world in which the “process of enforcing collective value judgments plays out in server farms rather than a messy deliberative and adjudicatory process.”
Engstrom and Gelbach examine the possible distributive effects of legal technology’s spread. They start with the promising notion that such tools might level the playing field between the “haves” and have nots,” letting smaller practices compete against BigLaw and making litigation for lower-value claims more cost-effective. But they warn that these tools might reproduce existing structural inequalities. Among other issues, few advanced tools are ready off-the-shelf, potentially putting them out of reach of smaller players.
Three individual case studies then explore how e-discovery, outcome-prediction, and advanced legal analytics tools change how litigation costs and information asymmetries play out. For each, Engstrom and Gelbach connect the tools to the applicable civil procedure doctrines and suggest how they might change in response. To provide a straightforward example, if legal technology tools reduce the costs associated with discovery of voluminous electronically stored information, a primary rationale for the restrictive Iqbal pleading standard would fall away.
Having laid the necessary groundwork, Engstrom and Gelbach draw out their lessons about the bi-directional interactions of legal technology and civil procedure. In the first instance, judges’ procedural decisions will set the incentives for legal technology tools, creating “a shadow innovation policy.” At the same time, these tools will change factors that inform existing civil procedure doctrine. These interactions and the policy decisions of judges and other rulemakers will “shape the future of American adversarialism” by determining how the power of advances in legal technology is allocated between parties vis-à-vis each other and judges.
This article weaves a rich story of how legal technology and civil procedure inform each other. It moves from concrete descriptions of specific tools and doctrine to analysis that illuminates the frequently unspoken underlying normative conceptions of adversarialism and justice that animate the civil justice system. And, while it is particularly timely because of how COVID-19 has forced the global legal community to rapidly integrate more legal technology tools into its practice, the article’s impact will extend well beyond this moment.
A final subtle beauty of the article is its invitation to legal scholars of all stripes to join the conversation. While Engstrom and Gelbach disclaim that the article exhaustively addresses all of the nuances of the issue, they highlight connections to virtually every major theme in civil procedure, providing a road map for further exploration of how legal technology interacts with access to justice, aggregate litigation, confidentiality, innovation, litigation as democratic deliberation, managerial judging, private procedural ordering, professional responsibility, settlements, and trans-substantivity (just to name a few).
Interesting, important, and not at all that much simple. By the way, new research, titled:
“AI in the Courtroom: A Comparative Analysis of Machine Evidence in Criminal Trials”
Here is the abstract:
Abstract
As artificial intelligence (AI) has become more commonplace, the monitoring of human behavior by machines and software bots has created so-called machine evidence. This new type of evidence poses procedural challenges in criminal justice systems across the world due to the fact that they have traditionally been tailored for human testimony.
This article’s focus is on information proffered as evidence in criminal trials which has been generated by AI-driven systems that observe and evaluate the behavior of human users to predict future behavior in an attempt to enhance safety. A poignant example of this type of evidence stemming from data generated by a consumer product is automated driving, where driving assistants as safety features, observe and evaluate a driver’s ability to retake control of a vehicle where necessary. In Europe, for instance, new intelligent devices, including drowsiness detection and distraction warning systems, will become mandatory in new cars beginning in 2022. In the event that human-machine interactions cause harm (e.g., an accident involving an automated vehicle), there is likely to be a plethora of machine evidence, or data generated by AI-driven systems, potentially available for use in a criminal trial.
It is not yet clear if and how this the data can be used as evidence in criminal fact-finding, and adversarial and inquisitorial systems approach this issue very differently. Adversarial proceedings have the advantage of partisan vetting, which gives both sides the opportunity to challenge consumer products offered as witnesses. By contrast, inquisitorial systems have specific mechanisms in place to introduce expert evidence recorded out-side the courtroom, including to establish facts, which will be necessary to thoroughly test AI.
Using the German and the U.S. federal systems as examples, this Article highlights the challenges posed by machine evidence in criminal proceedings. The primary area of comparison is the maintenance of trust in fact-finding as the law evolves to accommodate the use of machine evidence. This comparative perspective illustrates the enigma of AI in the courtroom and foreshadows what will become inevitable problems in the not-too-distant future. The Article con-cludes that, at present, criminal justice systems are not sufficiently equipped to deal with the novel and varied types of information generated by embedded AI in consumer products. It is suggested that we merge the adversarial system’s tools for bipartisan vetting of evidence with the inquisitorial system’s inclusion of out-of-court statements under specific conditions to establish adequate means of testing machine evidence.
Here:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3602038
Thanks