Ghodoosi & Kastner on Big Data on Contract Interpretation

Farshad Ghodoosi (Cal State U) and Tal Kastner (Touro Law) have posted “Big Data on Contract Interpretation” (UC Davis Law Review 2024) on SSRN. Here is the abstract:

This Article introduces macro contract research, a new methodology using big-data analytics to study private law. In doing so, it reveals significant trends that suggest the outsized role of corporations and the slide towards textualism in the development of contract law. This Article thereby sheds new light on enduring questions in contract scholarship and offers a novel approach applicable to other contexts.

Using California contract disputes as a case study, this Article uncovers data suggesting that courts are tipping a long-held balance in con-tract interpretation toward textualism. Deploying machine learning and an originally-trained algorithm, this Article uncovers data suggesting the diminishing role of individuals in contract litigation. At the same time, it suggests that corporations have played a central role in the development of contract law, a trend likely to increase in the future. As such, the Article contributes much-needed quantitative evidence to contract scholarship, which has long debated the centrality of corporate entities in shaping contract law but lacked the relevant empirical data.

This Article also makes a significant theoretical contribution. Scholarship has tended to overlook the particular operation of canons of contract, as opposed to statutory, interpretation, notwithstanding contract law’s distinct goal of enabling private ordering. The Article identifies the distinctive function of contract canons and offers a framework for their classification. Focusing on contract canons as a first step in gathering fundamental data on the development of contract law, this Article also presents a model for further large-scale empirical study.

Recommended.

Grossman, Grimm, Brown & Xu on The GPTJudge: Justice in a Generative AI World

Maura R. Grossman (U Waterloo; York U Osgoode Hall), Paul W. Grimm (Duke Law), Daniel G. Brown (U Waterloo), and Molly Xu (U Waterloo) have posted “The GPTJudge: Justice in a Generative AI World” (Duke Law & Technology Review 2023) on SSRN. Here is the abstract:

Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they are capable of producing computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI- generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property (“IP”) law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases.

This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by- step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world.

Bertomeu et al. on Capital Market Consequences of Generative AI: Early Evidence from the Ban of ChatGPT in Italy

Jeremy Bertomeu (Washington U – John M. Olin Business School, Yupeng Lin (NUS), Yibin Liu (same), and Zhenghui Ni (same) have posted “Capital Market Consequences of Generative AI: Early Evidence from the Ban of ChatGPT in Italy” on SSRN. Here is the abstract:

On March 31st, 2023, the Italian data protection authority found that ChatGPT violated data protection laws and banned the service in Italy, providing a natural experiment to assess the economic consequences of generative AI. Leveraging this event, we show that Italian firms with greater exposure to the technology exhibit an underperformance of around 9% compared to firms with lower exposure throughout the ban period. We observe a more significant negative impact on stock value for smaller and newly established companies, supporting a linkage to creative destruction. Bid-ask spreads widen during the ban, particularly for firms with fewer institutional investors, limited analyst coverage, and a lower presence of foreign investors. The evidence suggests a dual role of ChatGPT in enhancing firm productivity and investor information processing.

Li on Trusted and Trustworthy Algorithmic Fiduciaries

Yuning Li (Peking University School of Transnational Law) has posted “Trusted and Trustworthy Algorithmic Fiduciaries” on SSRN. Here is the abstract:

The Digital Age is speedily transforming into the Algorithmic Age, where algorithms carry on or contribute to a significant number of important social, economic, and technical decision-making. Apart from platform dominance, private governance, and surveillance capitalism, private and public use of algorithmic decision-making faces an increasingly severe legitimacy crisis of not being trusted or trustworthy. Three phases of Algorithmic Fiduciaries will accompany us into the ultimate human-centered symbiosis relations individually between human and intelligent algorithms. Trusted and trustworthy algorithmic fiduciaries are vitally important in maximizing the joint potentials and capabilities to significantly benefit the algorithms and the humanities.

This paper consists of three parts. First, it proposes the idea of “algorithmic fiduciary” and argues that the trusted and trustworthy algorithmic fiduciary is important. Second, it researches government policies and regulations, industry norms, civil society proposals, and other actions within the Government-Company-NGO Triangular on algorithmic decision-making in the European Union and the United States. Third, it evaluates how these actions impact trusted and trustworthy algorithmic fiduciaries.

Nay et al. on Large Language Models as Tax Attorneys

John Nay (Stanford Codex; NYU) et al. have posted “Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence” on SSRN. Here is the abstract:

Better understanding of Large Language Models’ (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and utilising the relevant legal authority to assess the impact of providing additional legal context to LLMs. Few-shot prompting, presenting examples of question-answer pairs, is also found to significantly enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels. As LLMs continue to advance, their ability to reason about law autonomously could have significant implications for the legal profession and AI governance.

Uuk, Gutierrez & Tamkin on Operationalising the Definition of General Purpose AI Systems

Risto Uuk (Future of Life Institute), Carlos Ignacio Gutierrez (same), and Alex Tamkin (same) have posted “Operationalising the Definition of General Purpose AI Systems: Assessing Four Approaches” on SSRN. Here is the abstract:

The European Union’s Artificial Intelligence (AI) Act is set to be a landmark legal instrument for regulating AI technology. While stakeholders have primarily focused on the governance of fixed purpose AI applications (also known as narrow AI), more attention is required to understand the nature of highly and broadly capable systems. As of the beginning of 2023, several definitions for General Purpose AI Systems (GPAIS) exist in relation to the AI Act, attempting to distinguish between systems with and without a fixed purpose. In this article, we operationalise these differences through the concept of “distinct tasks” and examine four approaches (quantity, performance, adaptability, and emergence) to determine whether an AI system should be classified as a GPAIS. We suggest that EU stakeholders use the four approaches as a starting point to discriminate between fixed-purpose and GPAIS.

Schrepel & Groza on The Adoption of Computational Antitrust by Agencies

Thibault Schrepel (VU Amsterdam; Stanford Codex; Sorbonne; Sciences Po) and Teodora Groza (Sciences Po) have posted “The Adoption of Computational Antitrust by Agencies: 2nd Annual Report” (3 Stanford Computational Antitrust 55 (2023)) on SSRN. Here is the abstract:

In the first quarter of 2023, the Stanford Computational Antitrust project team invited the partnering antitrust agencies to share their advances in implementing computational tools. Here are the 26 contributions we received.

Mik on The Automation of Contract Formation

Eliza Mik (Chinese University of Hong Kong – Law; Melbourne Law) has posted “Much Ado about Artificial Intelligence or: the Automation of Contract Formation” (International Journal of Law and Information Technology 2022) on SSRN. Here is the abstract:

Every day, millions of decisions are made based on information provided by computers and millions of transactions are concluded automatically, by means or with the assistance of computer programs. Computers can accept payment and dispense products. Computers can also calculate the optimal price and predict the demand for the product. Technology enables us to automate a wide range of tasks involved in the process of forming contracts. Contrary to popular belief, such novel transacting practices are easily accommodated by existing legal principles, at least when it comes to the common law of contract. The latter is technology neutral and generally disregards the manner the parties’ statements come into existence. Considerable uncertainty would result if the statements we see on our computer screens could be disavowed on the basis that they were the product of mindless computer operations. This paper contends that legal analyses must start with the law, not with overdramatized descriptions of technology or the assumption that contract law cannot accommodate such.

Pierce & Goutos on Why Law Firms Must Responsibly Embrace Generative AI

Natalie Pierce (Gunderson Dettmer; Columbia Law School; UC Berkeley) and Stephanie Goutos (Gunderson Dettmer; Albany Law School; The College of Saint Rose) have published “Why Law Firms Must Responsibly Embrace Generative AI” on SSRN. Here is the abstract:

In the era of artificial intelligence (AI), the professional axiom stands truer than ever: “AI won’t replace lawyers, but lawyers who use AI will replace lawyers who don’t.” This paper is a must-read for any forward-thinking law firm seeking to outpace competition and excel in an AI-augmented world, all while upholding the professional standards of ethical and client service that is not only competent, but exceptional.

The transformative impact of generative artificial intelligence (GAI) on the legal industry is inevitable, a change predicted to fuel global GDP growth by almost $7 trillion over the next decade. Amid growing concerns and even some calls for an outright prohibition of GAI in law firms, we argue for a balanced, responsible embrace of this technology. This stance, we believe, is imperative for the future of the legal profession and can position legal professionals at the forefront of innovation and client service. We provide several real-world examples of how organizations, including law firms, are already successfully leveraging GAI.

Our paper highlights how GAI, augmented with human input, can greatly improve the legal sector. Federal courts’ recent acknowledgement of GAI’s role in litigation, with requirements for its explicit disclosure, points towards its future ubiquity. In navigating this shift, we emphasize that lawyers must uphold their ethical standards and obligations outlined in the Model Rules of Professional Conduct.

We address and confront counterarguments suggesting GAI’s unsuitability for legal work, potential for ethics violations, and risks of inaccuracies, bias, privacy breaches, and legal risks. Acknowledging the inherent risks, we present strategies to mitigate these and continue competently delivering exceptional client service.

We also underline the risks of not using GAI, from potential unauthorized data disclosure to possible reputational damage and competitive disadvantage in the legal industry. Highlighting the importance of responsible GAI usage, we present a comprehensive list of the Top 10 best practices for its implementation within law firms. We conclude by stressing that legal professionals’ refusal to adopt GAI could lead to their obsolescence, predicting the prevalence of GAI policies across U.S. industries, including the legal sector, by 2023 end.