If artificial agents can behave empathically, could this contribute to the development of more ethical AI systems? This question becomes especially important and timely with the speed of development and increasing use-cases in AI, has been raising concerns about their ethical use. In this chapter, I tried to give a nuanced exploration of different components of computational empathy, and the opportunities and challenges they present on different stages of the moral decision-making process. Rather than offering a simple “yes” or “no” answer, it suggests that the integration of empathy into AI systems depends on which components are employed and when. The chapter reviews existing research on empathy’s connection to moral and ethical behavior in humans, followed by a critical examination of how these insights could be applied to artificial agents.

This chapter dives beneath the performatively egalitarian surface of Federal Rule of Evidence 702 (Testimony by Expert Witnesses), explores disparities in the rule’s application in civil and criminal cases, and applies queer theory to disrupt these norms and “forg[e] a social justice politics of change.” Queer theory is an interdisciplinary approach that dismantles traditional assumptions about gender and sexual identities in furtherance of social justice. Queering the law is not only about the use of gender and sexuality, it also aims to recognize and challenge traditional binaries and seek new possibilities. As Black queer author bell hooks remarked, “‘queer’ [is] about the self that is at odds with everything around it and that has to invent and create and find a place to speak and to thrive and to live.”

As readers of this volume will be well aware, Federal Rule of Evidence (FRE) 702 and the Supreme Court’s famous 1993 opinion in Daubert v. Merrell Dow Pharmaceuticals require courts to assess the reliability of expert evidence before admitting it to the jury. Most states impose similar rules. These rules are commonly understood to require courts to perform a gatekeeping function to keep unreliable expert evidence away from the jury.

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.

In this chapter, we will discuss the importance of routinely reflecting upon yourself as both a person and a prescriber. Identifying the types of situations that tend to make you feel most affiliative with your patients, as well as those which make you feel less attuned or empathic, can be helpful in identifying your own strengths and vulnerabilities. In this chapter, we discuss the importance of acknowledging our own subjectivity in the prescribing relationship, some common triggers of prescriber vulnerabilities, and provide some examples of situations where reflective practice is crucial.

Social justice in psychiatric prescribing necessarily elevates the personhood of the patient, in the context of the relationship with the provider as well as in the larger societal milieu. Grounded in a historical narrative, we offer practical tools to apply a social justice framework to prescribing practices. Simple steps advise the reader to consider practice context, utilize nuance in formulation, and pursue quality improvement in three common scenarios. At the end of the chapter, we offer reflection questions to encourage ongoing social justice in your psychiatric prescribing practice.

Undesirable bias is a well-known issue in algorithmic decision-making. This chapter explores how fairness is defined and whether it can be studied quantitively, and explores several debiasing frameworks from the literature.

Chapter 5 examines attitudes as predispositions that guide consumer responses, formed through the interplay of motivation, perception, and learning. It introduces the tripartite (ABC) model, distinguishing affective, behavioral, and cognitive components and explaining how they interact in evaluation and choice. The chapter then explains the theory of reasoned action (TRA) and the theory of planned behavior (TPB), which link attitudes, subjective norms, perceived behavioral control, and intentions to actual behavior. It analyzes how preference formation can generate biases that distort judgments and reinforce mindless consumption. Strategies and models of attitude change are reviewed, including how marketing can revitalize weakening attitudes, realign shifting ones, and resolve conflicting evaluations. The chapter culminates in a discussion of acceptance as open, nonjudgmental receptivity to one’s own and others’ perspectives, integrating self-compassion and empathy to support more mindful intentions and behaviors.

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.

Generative AI systems offer unprecedented capabilities for addressing access to justice challenges through their ability to understand and generate human-like text. This chapter examines how large language models (“LLMs”) can bridge the language gap between laypeople’s factual descriptions and legal concepts, providing personalized legal information and assistance with legal tasks.

The analysis explores several applications, including the automated provision of legal information using retrieval-augmented generation (“RAG”), streamlined legal intake processes, intelligent form completion assistance, and AI-enhanced mediation. These systems show promise for identifying legal issues from plain language descriptions, assisting with the drafting of legal documents, and supporting dispute resolution processes.

However, significant challenges remain, particularly regarding hallucinations where models generate convincing but incorrect information, algorithmic bias that may perpetuate existing inequalities, and transparency concerns inherent in black-box AI systems. While generative AI holds promise for democratizing legal assistance, implementation with robust safeguards, continuous evaluation, and human oversight is essential to ensure these tools enhance rather than undermine access to justice.

Online Dispute Resolution (“ODR”) emerged from the convergence of Alternative Dispute Resolution practices and digital platforms, addressing the need for more accessible dispute resolution mechanisms. ODR platforms reduce logistical burdens by eliminating travel requirements and enabling asynchronous communication, while helping to level the playing field between represented and unrepresented parties.

This chapter examines how AI enhances ODR through automated settlement calculations using game theory, negotiation analysis through discourse monitoring, and sentiment analysis to detect contentious exchanges. AI systems can also assist facilitators by analyzing negotiation patterns and suggesting when interventions may be needed.

Despite significant potential benefits, ODR faces challenges, including the digital divide, algorithmic bias, and jurisdictional enforceability. Well-implemented ODR platforms, supported by transparent standards and ethical AI principles, may significantly contribute to making dispute resolution more equitable and responsive to modern societal needs, while requiring careful attention to user-centered design and robust data protection measures.

The introduction outlines the book’s core questions about justice delivery in India. It introduces the concept of “multistage” discrimination, and presents the enclave theory as a means to assess criminal justice interventions like all-women police stations.

Chapter 1 specifically examines racial inequity in the Social Security Old Age/Retirement Insurance program and focuses largely on the original 1935 Social Security Act’s exclusion of disproportionately Black agricultural and domestic workers from coverage. It directly challenges and critiques the Social Security Administration’s public historian’s relatively recent conclusion that race played no meaningful role in the social insurance program exclusion’s adoption and resulting impacts on African Americans and similarly situated persons of color. It also explores, in less depth, discrimination facilitated through local administration of the means-tested welfare programs, and contemporaneous and intertwined racially infused legislative history of the respective means-tested and social insurance old age programs enacted in Titles I and II of the 1935 Act. It includes analysis of the influence of southern legislators’ efforts to preserve the postbellum plantation and sharecropping system’s exploitation of Black farmworkers on each program’s design. It also examines this statutory exclusion through the lens of equal protection doctrine to inform further the inquiry into the salience of race in the exclusion’s enactment.

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.

This chapter explores case-control studies, an analytical observational design particularly suited for investigating rare diseases and conditions with long latency periods. The methodology is explained, with cases defined as individuals with the outcome of interest and controls as those without, drawn from the same source population. Retrospective assessment of exposure history forms the basis of analysis, allowing researchers to identify associations between exposures and outcomes. The odds ratio is introduced as the principal measure of association, with guidance on its interpretation and limitations. The chapter highlights the efficiency of case-control studies, requiring smaller sample sizes compared with cohort designs, and their particular value when incidence data are difficult or costly to obtain. Challenges such as recall bias, selection bias, and difficulties in establishing temporality are critically discussed. Strategies to minimise bias, including careful matching of cases and controls and validation of exposure measures, are outlined. Practical examples demonstrate the application of case-control studies in fields such as cancer epidemiology and outbreak investigations. The chapter concludes by emphasising the continued relevance of case-control designs in modern epidemiology. This chapter maps to syllabus section 3.2.3, which requires detailed knowledge of case-control studies, their strengths, weaknesses, and potential biases.

Chapter 3 reviews understandings of ideology and concludes with a discussion of Verschueren’s characterization of ideology and its manifestations in discourse. These theses, with the attributes of ideology identified by Eagleton, provide the bases for the arguments that Amnesty’s appeals and related documents are indeed elements of ideological discourse. According to Eagleton, ideologies are not necessarily instruments of false consciousness; they are constructed to promote the interests of a group, motivate its members to act in the group’s interest, legitimate those interests and actions by presenting them as natural, universal, normative and rational. The analysis shows that while the appeals are designed to persuade their addressees, they are also intended to motivate AI members. The chapter addresses Verschueren’s characterizations of ideology: rarely questioned and potentially hegemonic language use; a normative aspect of social reality manifested across multiple discourse genres; immune to experience and observation often represented implicitly and resistant to explicit expression. It frames, validates, explains and legitimates attitudes, states of affairs, and actions.

Research is another area of social work subject to racial biases. Historically, research was used as a weapon against BIPOC, designed to “prove” their inferiority. While less overtly racist nowadays, research continues to promote a racial divide through several different means: overreliance on a positivist paradigm for researching social questions; holding on to theories based on white normative assumptions, failure to proportionally include people of color in sampling, or include them in defining the research question in cultural and locational contexts.

Chapter 2 argues how advancements in methods and practices of archaeological research have led to conceptual transformations and remarkable implications in the understanding of archaeological landscapes.