Naïve Realism, Cognitive Bias, and the Benefits and Risks of AI

In this short piece I comment on Orly Lobel\u27s book on artificial intelligence (AI) and society The Equality Machine. Here, I reflect on the complex topic of aI and its impact on society, and the importance of acknowledging both its positive and negative aspects. More broadly, I discuss the various cognitive biases, such as naïve realism, epistemic bubbles, negativity bias, extremity bias, and the availability heuristic, that influence individuals\u27 perceptions of AI, often leading to polarized viewpoints. Technology can both exacerbate and ameliorate these biases, and I commend Lobel\u27s balanced approach to AI analysis as an example to emulate. Although AI is changing at an unprecedented rate, as exemplified by recent advances in Large Language Model (LLM) technology such as ChatGPT/GPT4, humans are adaptable, and society can actively steer toward a desirable future. By acknowledging the potential benefits and risks of AI, and by striving to overcome inherent cognitive biases, individuals can achieve a more balanced understanding of the technology and its impact on society

The Variable Determinacy Thesis

This Article proposes a novel technique for characterizing the relative determinacy of legal decision-making. I begin with the observation that the determinacy of legal outcomes varies from context to context within the law. To augment this intuition, I develop a theoretical model of determinate legal decision-making. This model aims to capture the essential features that are typically associated with the concept of legal determinacy. I then argue that we can use such an idealized model as a standard for expressing the relative determinacy or indeterminacy of decision-making in actual, observed legal contexts. From a legal theory standpoint, this approach - separating determinacy and indeterminacy into their constituent conceptual elements - helps us to more rigorously define these theoretical ideas. Ultimately, from a practical standpoint, I assert that this framework assists in understanding why legal outcomes in certain contexts are determinate enough to be amenable to resolution by computers

Efficient Uncertainty in Patent Interpretation

Research suggests that widespread uncertainty over the scopes of issued patents creates significant costs for third-party firms and may decrease innovation. This Article addresses the scope uncertainty issue from a theoretical perspective by creating a model of patent claim scope uncertainty. It is often difficult for third parties to determine the legal coverage of issued patents. Scope underdetermination exists when the words of a patent claim are capable of a broad range of plausible scopes ex ante in light of the procedures for interpreting patents. Underdetermination creates uncertainty about claim coverage because a lay interpreter cannot know which interpretation will ultimately be elected and employed by a judge or jury in a patent infringement proceeding. This Article models this uncertainty problem by the set of interpretations that are plausible for a patent-claim element in light of constraints that restrict meaning, internal and external to the patent document. The model suggests generalizable properties against which we can critically evaluate patent interpretive rules and procedures. On this basis, the Article develops an approach to improving the ex ante scope precision of any given patent claim. The general approach is to reduce the set of interpretative scopes that patent claim words can plausibly obtain. By increasing explicit, scope-defining information in the public patent record, it is possible to improve scope precision by ex ante clarifying scope coverage and exclusion in foreseeable scope uncertainty scenarios

Structural Rights in Privacy

This Essay challenges the view that privacy interests are protected primarily by law. Based upon the understanding that society relies upon nonlegal devices such as markets, norms, and structure to regulate human behavior, this Essay calls attention to a class of regulatory devices known as latent structural constraints and provides a positive account of their role in regulating privacy. Structural constraints are physical or technological barriers which regulate conduct; they can be either explicit or latent. An example of an explicit structural constraint is a fence which is designed to prevent entry onto real property, thereby effectively enforcing property rights. Latent structural constraints, by contrast, are the secondary costs arising from the technological state of the world which implicitly regulate conduct by making certain activities too difficult to engage in on a widespread basis. Society relies upon these latent structural constraints to reliably inhibit certain unwanted conduct in a way that is functionally comparable to its use of law. For example, society has frequently depended upon the search costs involved in aggregating and analyzing large amounts of information to effectively protect anonymity. The operation of these latent structural constraints is often implicit and therefore non-obvious to policymakers. This focus on implicit, rights-like relationships which are protected by nonlegal constraints becomes significant because latent structural constraints are vulnerable to sudden dissipation due to emerging technologies. This Essay describes a conceptual framework by which policymakers can explore this association between constrained behavior and latent structural constraints and suggests that they employ this conceptualization in order to identify non-obvious privacy interests which may be threatened by emerging technologies

Artificial Intelligence and Law: An Overview

Much has been written recently about artificial intelligence (AI) and law. But what is AI, and what is its relation to the practice and administration of law? This article addresses those questions by providing a high-level overview of AI and its use within law. The discussion aims to be nuanced but also understandable to those without a technical background. To that end, I first discuss AI generally. I then turn to AI and how it is being used by lawyers in the practice of law, people and companies who are governed by the law, and government officials who administer the law. A key motivation in writing this article is to provide a realistic, demystified view of AI that is rooted in the actual capabilities of the technology. This is meant to contrast with discussions about AI and law that are decidedly futurist in nature. That body of work speculates about the effects of AI developments that do not currently exist and which may, or may not, ever come about. Although those futurist conversations have their place, it is important to acknowledge that they involve significant, sometimes unsupported, assumptions about where the technology is headed. That speculative discussion often distracts from the important, but perhaps less exotic, law and policy issues actually raised by AI technology today

Computable Contracts

This Article explains how and why firms are representing certain contractual obligations as computer data. The reason is so that computers can read and process the substantive aspects of contractual obligations. The representation of contractual obligations in data instead of (or in addition to) the traditional written language form - what this Article calls data-oriented contracting - allows for the application of advanced computer processing abilities to substantive contractual obligations. Certain financial contracts exemplify this model. Equity option contracts are routinely represented not as contract documents written in ordinary language - but as data records intended to be processed by computers. The parties incorporate such data as an expression of their substantive contractual memorialization through various processes. The representation of contractual obligations as data allows for new contracting properties. Among these possibilities is the design of computable contract terms. This Article explains how parties can effectively translate certain contractual criteria into a comparable set of computer-processable rules. Parties can provide computer systems with existing data that is indicative or relevant to compliance or performance. In this way, certain previously manual comparisons between promised terms and actual party activities can be automated. This can have the effect of significantly reducing transaction costs associated with contract monitoring and compliance as compared to the traditional written language contracting paradigm

Artificial Intelligence and Law: An Overview

Much has been written recently about artificial intelligence (AI) and law. But what is AI, and what is its relation to the practice and administration of law? This article addresses those questions by providing a high-level overview of AI and its use within law. The discussion aims to be nuanced but also understandable to those without a technical background. To that end, I first discuss AI generally. I then turn to AI and how it is being used by lawyers in the practice of law, people and companies who are governed by the law, and government officials who administer the law. A key motivation in writing this article is to provide a realistic, demystified view of AI that is rooted in the actual capabilities of the technology. This is meant to contrast with discussions about AI and law that are decidedly futurist in nature

Response, Bridges II: The Law--STEM Alliance & Next Generation Innovation

Technological change recently has altered business models in the legal field, and these changes will continue to affect the practice of law itself. How can we, as educators, prepare law students to meet the challenges of new technology throughout their careers