Edwin O. Eboigbe (U Illinois College Law) has posted “AI in Legal Analytics: Balancing Efficiency, Accuracy, and Ethics in Contract and Predictive Analysis” on SSRN. Here is the abstract:

In the pursuit of clarity in legal language, major strides have been made to simplify the use of legal terminology and make it more accessible. However, as the legal field evolves, the increasing reliance on data and quantitative analysis has transformed how legal information is processed and interpreted. Law reports and other legal sources and materials are now analyzed by parameters and systems developed using complex mathematical expressions and representations. Data analytics has proven wrong with the assertion that Law and Science do not intersect. To succinctly put, Law produces two forms of data namely, structural and un-structural data. While structural data includes numbers which ultimately relate to the number of cases filed in court, how many times someone has been indicted for a crime, the number of cases won by a lawyer etc. un-structural Data on the other hand is represented in the form of legal briefs, judgments, contractual documents, etc. Technology, through different phases of continuous advancement, has aided the transformation of law and how Legal data is produced and analyzed. At the core of this transformation is the emergence of Artificial Intelligence (AI) which has been a subject of discussion across major sectors and is gradually advancing and reshaping the practice of law. This work will analyze how the use of AI has greatly revolutionized the Legal system, improving the quality and quantity of legal work. The shortcomings faced by these systems will also be discussed as well as possible recommendations. This author will conclude that although AI cannot fully replace legal workers, especially because the practice of law requires some form of discretion and judgment only humans can make, It is however shrewd to completely integrate AI into legal work in order to create unprecedented advancements in the Legal system.

David Atkinson (The U Texas Austin) has posted “Putting GenAI on Notice: GenAI Exceptionalism and Contract Law” on SSRN. Here is the abstract:

Gathering enough data to create sufficiently useful training datasets for artificial intelligence and other purposes requires scraping most public websites. The scraping is conducted using pieces of code (scraping bots) that make copies of website pages. Today, there are only a few ways for website owners to effectively block these bots from scraping content. One method, prohibiting scraping in the website terms of service, is loosely enforced because it is not always clear when the terms are enforceable. This paper aims to clear up the confusion by describing what scraping is, how entities do it, what makes website terms of service enforceable, and what claims of damages website owners may make as a result of being scraped. The novel argument of the paper is that when (i) a site’s terms prohibit scraping and (ii) a bot scrapes all the pages on the website including those terms, the bot’s deployer has actual notice of the terms and those terms are therefore enforceable, meaning the site can claim a breach of contract. This paper details the legal and substantive arguments favoring this position while cautioning that nonprofits with a primarily scientific research focus should be exempt from such strict enforcement.

Yonathan A. Arbel (U Alabama Law) has posted “Time & Contract Interpretation: Lessons from Machine Learning” (in Research Handbook on Law & Time, F. Fagan & S. Levmore eds. 2025) on SSRN. Here is the abstract:

Contract interpretation is the task of estimating what distant in time parties meant to say or would have said about a specific contingency. For at least a century, scholars and courts have been debating how to best carry out this task.

Conceiving of the interpretative task as one of prediction, I suggest that there are some valuable lessons to be drawn from a field devoted to building prediction models: machine learning. From this viewpoint, this chapter makes four contributions to the study of contract interpretation. It first defends the view of interpretation-as-prediction against the common linguistic view. The linguistic view perceives interpretation as establishing meaning in the philosophy of language sense. But as applied to contract interpretation, such arguments often employ motte-and-bailey argumentation. The second is in explaining a puzzling aspect of the debate about interpretative methods. Both textualists and contextualists insist that their method is more accurate. They can do so because they conflate two senses of the term, precision and accuracy. Third, it brings the hard problem of bias-variance tradeoff to the choice of interpretative methods. Finally, and most speculatively, the chapter distinguishes between interpretation and simulation, and argues that the latter is far more important but far less understood in legal theory. With advances in modeling techniques, the idea of simulation demands serious reconsideration.

David A. Hoffman (U Penn Law) and Yonathan A. Arbel (U Alabama Law) have posted “Generative Interpretation” (NYU Law Review 2024) on SSRN. Here is the abstract:

We introduce generative interpretation, a new approach to estimating contractual meaning using large language models. As AI triumphalism is the order of the day, we proceed by way of grounded case studies, each illustrating the capabilities of these novel tools in distinct ways. Taking well-known contracts opinions, and sourcing the actual agreements that they adjudicated, we show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties’ agreements. We also illustrate how models can calculate the probative value of individual pieces of extrinsic evidence.

After offering best practices for the use of these models given their limitations, we consider their implications for judicial practice and contract theory. Using LLMs permits courts to estimate what the parties intended cheaply and accurately, and as such generative interpretation unsettles the current interpretative stalemate. Their use responds to efficiency-minded textualists and justice-oriented contextualists, who argue about whether parties will prefer cost and certainty or accuracy and fairness. Parties—and courts—would prefer a middle path, in which adjudicators strive to predict what the contract really meant, admitting just enough context to approximate reality while avoiding unguided and biased assimilation of evidence. As generative interpretation offers this possibility, we argue it can become the new workhorse of contractual interpretation.

Recommended.

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.

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.

Thibault Schrepel (University Paris 1 Panthéon-Sorbonne; VU University Amsterdam; Stanford University’s Codex Center; Sciences Po) has posted “Smart Contracts and the Digital Single Market Through the Lens of a ‘Law + Technology’ Approach” on SSRN. Here is the abstract:

The deployment of smart contracts within the European zone could fluidify economic transactions. It also risks fragmenting the Digital Single Market (“DSM”). This conundrum calls for a constructive response to preserve both the benefits brought by smart contracts and a strong DSM.

Against this background, this report adopts a “law + technology” approach. It suggests combining law and technology to develop solutions that encourage the evolution of smart contracts (rather than hindering it) in a direction that preserves and reinforces the DSM.

Ernest Lim (National University of Singapore – Faculty of Law) has posted “B2B Artificial Intelligence Transactions: A Framework for Assessing Commercial Liability” on SSRN. Here is the abstract:

Business to business (“B2B”) artificial intelligence (“AI”) transactions raise challenging private law liability issues because of the distinctive nature of AI systems and particularly the new relational dynamics between AI solutions providers and procurers. This article advances a three-stage framework comprising data management, system development and implementation, and external threat management. The purpose is to unpack AI design and development processes involving the relational dynamics of providers and procurers in order to understand the parties’ respective responsibilities. Applying this framework to English commercial law, this article analyses the potential liability of AI solutions providers and procurers under the Supply of Goods and Services Act and the Sale of Goods Act. The assumption that only AI solutions providers will be subject to liability, or that no party will be liable due to the “autonomous” nature of AI systems, is rejected.

W. Gregory Voss (TBS Business School) has posted “Data Protection Issues for Smart Contracts” (Smart Contracts: Technological, Business and Legal Perspectives (Marcelo Corrales, Mark Fenwick & Stefan Wrbka, eds., 2021) on SSRN. Here is the abstract:

Smart contracts offer promise for facilitating and streamlining transactions in many areas of business and government. However, they also may be subject to the provisions of relevant data protection laws such as the European Union’s General Data Protection Regulation (GDPR) if personal data is processed. Initially, this chapter discusses the data protection/data privacy distinction in the context of differing legal models. However, the focus of analysis is the GDPR, as the most significant and influential data protection legislation at this time, given in part to its omnibus nature and extraterritorial scope, and its application to smart contracts.

By their very nature, smart contracts raise difficulties for the classification of the various actors involved, which will have an impact on their responsibilities under the law and their potential liability for violations. The analysis in this chapter turns on the roles of the data controller in the context of smart contracts, and this contract review the definition of that term and of ‘joint controller’ considering supervisory authority guidance. In doing so, the signification of the classification is highlighted, especially in the case of the GDPR.

Furthermore, certain rights granted to data subjects under the GDPR may be difficult to provide in the context of smart contracts, such as the right to be forgotten/right to erasure, the right to rectification and the right not to be subject to a decision based solely on automated processing. This chapter addresses such issues, together with relevant supervisory authority advice, such as the use of encryption to make data nearly inaccessible to approach as nearly as possible the same result as erasure. On the way, the important distinction between anonymized data and personal data is explained, together with its practical implications, and requirements for data integrity and confidentiality (security) are detailed.

In addition, the GDPR requirement of privacy by design and by default must be respected, when that that legislation applies. Data protection principles such as purpose limitation and data minimisation in the case of smart contracts are also scrutinized in this chapter. Data protection and privacy must be considered when smart contracts are designed. This chapter will help the reader understand the contours of such requirement. Even for jurisdictions outside of the European Union, privacy by design will be interesting as best practice.

Finally, problems related to cross-border data transfers in the case of public blockchains are debated, prior to this chapter setting out key elements to allow for a GDPR-compliant blockchain and other concluding remarks.

William Lehr (MIT) has posted “Smart Contracts, Real-Virtual World Convergence and Economic Implications” on SSRN. Here is the abstract:

Smart Contracts (SCs) are usually defined as contracts that are instantiated in computer-executable code that automatically executes all or parts of an agreement with the assistance of block-chain’s distributed trust technology. This is principally a technical description and results in an overly narrow focus. The goal of this paper is to provide an overview of the rapidly evolving multidisciplinary literature on Smart Contracts to provide a synthesis perspective on the economic implications of smart contracts. This necessitates casting a wider-net that ties SCs to the literature on the economics of AI and the earlier Industrial Organization literature to support speculation about the role of SCs in the evolution of AI and the organization of economic activity. Accomplishing this goal builds on a repurposing of the Internet hourglass model that puts SCs at the narrow waist between the real (non-digital) and virtual (digital) realms, serving as the connecting glue or portal by which AIs may play a larger role in controlling the organization of economic activity.