In Chapter 7, the objectification of humanity, resulting in the cataloging of humans through the means of science and technology, is addressed through the example of US sex offender registries and other criminal databanks. Human cataloging offers government a means to control society in the name of public order, but law reform offers feasible methods of resisting the encroachment on rights of privacy and liberty.

In Chapter 4, we turn to the contemporary manifestations of the managerial approach. To the extent that we view digital technology as a problem, we tend to think of it as a problem with managerial solutions. In other words, we tend to believe that the right experts, in the right institutions, focusing on the right questions, could make the harms of digital technology disappear. Some want to erect new administrative agencies or private “oversight boards” to govern digital life; some want to empower courts to address the “addictive” features of social media; others prefer to use public officials and NGOs to put pressure on technology platforms themselves. In each of these cases, reformers call on private or public managers to resurrect the goods of robust associations without defending associations themselves. We show that seemingly disparate efforts to address “digital addiction,” fight the “loneliness epidemic,” combat “misinformation,” regulate AI development, and reimagine “privacy” all fall into this same trap. These are often misguided attempts to respond to problems better understood and addressed as the consequences of a social order in which intermediary associations lack the three powers.

Privileges are generally disfavored as impeding the truth. But in this chapter, I propose a novel way to think of privilege altogether: privilege as a means of resistance, a tool to oppose unjust systems and laws. Current privilege scholarship essentially assumes faith in the adversarial legal system and the interest in soliciting, collecting, and admitting all available evidence. This assumption implicitly accepts that the adversarial system is valid, and the substantive law that privilege limits is also valid. However, if we question the legitimacy of the adversarial system and the substantive laws it upholds, the cost-benefit analysis shifts. Privilege could serve as an instrument of resistance by prioritizing the preservation of relationships over the legal system’s pursuit of truth.

This chapter introduces responsible AI research as part of professional practice. It explains why strong performance and reproducibility are not enough when systems can amplify bias, threaten privacy, impose environmental costs, or be misused. The chapter highlights how to think about downstream impacts, dual-use risks, and misalignment, and how context and deployment shape what “responsible” means. It offers practical guidance on building habits that make research safer and more credible, including stating assumptions, reporting limitations, and considering human oversight.

This chapter explains how PhD students can use large language models (LLMs) to support research without outsourcing scholarship. It covers practical uses in writing, literature exploration, coding, experiment planning, and self-critique, while emphasizing that you remain responsible for accuracy, reasoning, and originality. The chapter highlights common failure modes such as fabricated citations, confident errors, and overly agreeable “reviews,” and discusses when LLM feedback is helpful versus misleading. It also outlines responsible use, including disclosure norms, bias awareness, and protecting confidential data. The goal is to benefit from speed and breadth while staying intellectually in control.

Presents some quantitative evidence about cooking and eating practices within the quarters, suggesting enslavers’ regimentation of food cultures increased over time, but was not motivated by size of holding. Briefly considers how rationing enabled cooking in the quarters, probing some overlooked aspects of food allocation, such as who received rations. Explores “blended” systems of food regimentation, including the specificities of these systems according to type of labor performed, and at exceptional times of year such as the annual harvest. Highlights how customs on Sundays were different and distinctive. Investigates cooking and eating patterns within the relative privacy of the quarters, including how enslaved people supplemented their often scant and monotonous diets through family-based or collective gardens, as well as by hunting and fishing – usually the preserve of men and boys. Illustrates how enslaved people sought to cook and eat autonomously and independently from their enslavers, including collectively, but within their own extended families and wider communities. Provisioning could burden women, but was also a source of pride and an expression of love.

This chapter traces the intellectual and technological lineage of Bitcoin and digital money. While Nakamoto’s white paper launched Bitcoin, its roots extend through decades of economic theory, cryptographic innovation, and activist movements. We examine how the Austrian and Chicago Schools of Economics provided a framework for stateless and non-inflationary money, and how Cypherpunk ideals shaped the push for privacy and decentralization. The chapter reviews early experiments with digital currencies such as DigiCash, b-money, and e-gold, highlighting the technical shortcomings, regulatory battles, and user adoption barriers that prevented their success but furnished essential building blocks for Bitcoin. We then contrast the classical financial attributes of money—medium of exchange, unit of account, and store of value—with additional digital requirements such as offline spendability, identity-less spendability, and fungibility. Finally, we show how Bitcoin resolved the long-standing double-spending problem without a central authority through Proof of Work, situating it as both a culmination of earlier efforts and the starting point for a new era of cryptocurrencies.

This chapter examines two dimensions of AI assurance for systems designed to assist unrepresented litigants: (1) reliability and (2) privacy and accountability. Reliability encompasses accuracy, calibration, robustness, and bias. Privacy and accountability involve balancing data needs with the protection of sensitive information and ensuring the responsible deployment of AI. These considerations take on heightened importance given unrepresented litigants’ unique vulnerabilities and legal contexts.

Reliability standards must be higher for unrepresented litigants, who cannot verify AI outputs, than for legal professionals, who can exercise oversight. Privacy and accountability challenges arise from AI systems’ need for vast training datasets containing sensitive legal information, creating tensions between the effectiveness of these systems and data protection rights under frameworks such as the General Data Protection Regulation (“GDPR”) and the EU AI Act.

Effective AI assurance requires interdisciplinary collaboration between technologists and legal stakeholders to develop appropriate methodologies for defining, measuring, and verifying both reliability and privacy protections in legal AI systems.

This chapter provides a foundational introduction to generative AI and large language models, addressing their capabilities and limitations. These models can perform competitively with humans on standardized exams and legal reasoning tasks and can be applied flexibly to legal challenges through natural language prompts.

However, significant limitations constrain their deployment in legal contexts. Models produce hallucinations, generating confident but incorrect information that can mislead users making critical legal decisions. The ELIZA effect compounds this risk as users tend to anthropomorphize AI and overestimate its capabilities. Additional challenges include potential biases introduced by the training data, privacy concerns associated with hosted models, and substantial computational costs. Understanding both the opportunities and limitations is crucial for safely deploying generative AI to address access-to-justice challenges.

Human genetic information is best understood as a non-rivalrous and non-excludable social resource, making it well suited to commons-based governance as a complement to state- and market-led models. Using the case of deCODE Genetics in Iceland, the chapter shows the practical viability of such an approach, underscoring the importance of public cooperation, ethical safeguards, and consent. Yet the model faces a central dilemma: the need for broad data sharing to advance research versus the individual participant’s right to privacy. The chapter reframes this tension by conceptualizing privacy not as the negation of sharing but as one of its dimensions. It then resolves the dilemma by proposing a participatory, procedurally legitimate system in which stakeholders (including data contributors, researchers, and clinicians) collectively determine rules of access, use, and privacy through democratic deliberation. This approach moves beyond top-down declarations and instead establishes a self-governing genomic commons. A mutual benefit, procedurally democratic framework offers a promising path to realize the genome’s potential for public health while safeguarding individual rights.

This chapter critically reviews the success of existing algorithms in explaining and predicting human behaviour. While traditional statistical methods have limitations in this area, algorithmic approaches have gained popularity. The chapter covers a range of algorithms, including decision trees, neural networks and clustering algorithms, evaluating their strengths and limitations in various applications. It also considers ethical concerns, such as bias and privacy violations and the need for transparency and explainability. The chapter emphasises the importance of interdisciplinary collaboration between computer science, statistics and behavioural science, and the need for ongoing development and refinement of these methods. By evaluating the effectiveness of algorithmic approaches to human behaviour, this chapter is a valuable resource for researchers and practitioners in the field.

This chapter is about trust. It is the only chapter in the book that considers the opinions of professionals who had not used machine translation at work. Most of these professionals were prepared to use machine translation if needed. Some were enthusiastic about it. Others had reservations which they would want to see addressed before deciding to use a machine translation tool. These reservations included: (1) concerns about accuracy; (2) concerns about privacy and confidentiality; (3) perceptions of social and professional norms and whether their use of machine translation would be accepted by others; and (4) concerns about how machine translation may lack ‘the human touch’ in sensitive interactions. The chapter examines human communication in relation to the concept of empathy and AI’s ability to mimic it. It ends with a discussion of workplace training by exploring what machine translation users would like to see covered in initiatives aimed at enhancing their trust judgment abilities.