In Chapter 1, we set the scene by examining the dynamics of online offensive language. We examine offensive language across a spectrum, ranging from non-polite expressions to grossly offensive (potentially illegal) speech. We also explore the conceptual links between offensive language and related notions such as impoliteness, hate speech and language aggression. Importantly, this chapter focuses on why understanding offensive language is, above all, a concern best addressed by linguists. To achieve this, we discuss the similarities and differences between grossly offensive and (im)polite language. We specifically focus on pragmatic concepts such as locution, illocution and perlocution to explain how they operate at both ends of the spectrum. Finally, we address the challenges of detecting offensive language in computational approaches to combating online hate, emphasising the vital role of linguistic contributions.

Chapter 3 explains our research methods, data collection and ethics. In this chapter, we focus on how we have addressed the typical challenges of interpreter bias in studying online offensive language. To mitigate these issues, we ground our study in naturally occurring data flagged as offensive by the targets themselves. We also introduce the reader to the world of social media content creators and discuss who the targets in our study are, and provide some demographic information about them. We discuss the use of Sketch Engine as a tool to address our primary objective of exploring the formal, lexical, semantic and discursive strategies involved in the construction of offensive language. We also discuss Wmatrix5, a corpus tool used to explore the semantic dimensions of offensive language. In addition, we explain how quantitative analysis was combined with thematic and linguistic-pragmatic approaches to examine how frequency and context shape offensive meanings. Chapter 3 further outlines our ethical considerations, including the responsible handling of data and the protection of participant anonymity, throughout the research process.

Impartiality, broadly meaning the absence of bias and according equal treatment before the law, is a foundational element of judicial decision-making around the world. In this chapter, we consider how the goal of judicial impartiality may be either enhanced and supported or undermined by the use of artificial intelligence. Key developments in legal AI include innovations directed toward courts and decision- makers. These may be process-driven – for example, triaging or decision-supporting systems; in the case of pre-trial processes, judges may need to manage technology-facilitated document discovery. AI systems may also be involved in the production of evidence submitted to the court. Finally, courts and judges themselves may be the subjects of AI tools, such as those which identify patterns in decision-making. As this chapter explores, these different uses all have implications for the way that judicial impartiality is enacted and tested.

This chapter summarises the key findings of the book on offensive language online, with particular emphasis on the perspectives of those targeted. It outlines the book’s multi-layered approach, which integrates corpus linguistics, discourse analysis and pragmatics. The chapter also explores future directions for research, including platform-specific variations, sociolinguistic changes and the value of interdisciplinary methodologies for understanding and addressing offensive language in digital spaces.

Among the key constructs of biomedical research (random error [chance], risk, and bias in the search for causation), bias (or systemic error) is the most formidable source of inefficient and wasteful research, leading to incorrect or exaggerated results. The cause of most disease is complex, owed to many inherent (genetic) and environmental risk factors. It is in studying the interplay of these, each incurring modest risk, that many biases come into play.

This chapter furthers and advances the decolonial process discussed in previous chapters by providing a deeper understanding of the totality of anti-oppressive practice and its embedding into direct clinical work through the clinical supervision process. Special attention is paid to complimentary theories and concepts (e.g. the cycle of socialization, relational-cultural theory, building allyship, dismantling racism, inclusive supervision and followership, etc.) that strengthen anti-oppressive decolonial supervision and leadership practice.

Chapter 8 explains why there has been so much enthusiasm for integrating AI into multiple dimensions of the hiring process, from resume screening to interview bots, despite these endeavors being marred by fundamental flaws, including, in some cases, integrating bias, unreliable pseudoscientific methods, and dehumanizing interactions. In addition to analyzing the incentives that have motivated companies to use flawed, innovative tools, we provide a road map for how to develop and use responsible AI upgrades in the hiring process.

There is little written about ethics consultation in a post-acute environment. Applying ethics consultation expectations from the acute care world would be a disservice to the healthcare continuum and those that support the homecare environment. This chapter aims to expose the challenges that face those caring for patients as guests in their home, in order to open a much-needed dialogue and opportunity for bidirectional learning that ensures these voices are represented. A home-based hospice team requests an ethics consult for a patient that they deem is "unsafe" for the staff to continue to care for. Staff distress arose in a recent joint visit with nursing and social work when there were persons who were described as being aggressive. This case consult went sideways very quickly. The leadership for the team caring for the patient came with a preconceived notion of the outcome and verbalized feeling untrusted by the ethics committee. Similarly, the ethics committee was divided on the case and committee members verbalized “giving up” when a consensus could not be reached. Members of the ethics committee reflect on the various haunting aspects - both individually and collectively - and the need to balance patient rights and staff safety in a post-acute environment. This case brought about significant organizational changes in ethics consultation, which are shared with the reader.

How much do we care when no one is looking? A patient with critical injury and vulnerable to bias—as an uninsured Person of Color experiencing homelessness and social isolation, with a history of mental illness and drug use— experiences barriers to receiving necessary treatment and standard care. When a patient is unable to ask for help, and has no family member or friend to help, what standard of care can they hope to receive? Can the quality of care provided to unrepresented patients represent a hospital’s culture of care? The writer wonders whether to “stay in my lane” and focus only on the ethical question prompting consultation, or if the principles of beneficence and nonmaleficence justify speaking up about substandard care. To mitigate the risk of acting as the “ethics police” by engaging in micromanagement of patient care, the writer describes efforts to expand ethics’ scope to change systemic and cultural attitudes by establishing preventative measures to identify and combat bias and preemptive judgments of futility.

Our chapter explores the ethical and systemic challenges faced by healthcare staff when caring for an adolescent patient boarding in the emergency room (ER). We use the case of Samantha, an indigenous adolescent, brought to the ER after trying to elope from her youth treatment center. Samantha’s prolonged stay in the ER highlights significant gaps in healthcare; society’s struggle to address the needs of vulnerable populations; and the healthcare staff’s efforts to fulfill the duties of beneficence, nonmaleficence, and justice. Samantha’s case underscores the need for improved clinical and institutional processes and support systems. We advocate for better access to ethics support, enhanced community resources, and a more inclusive approach to care that considers the unique needs of marginalized individuals. Our case also reflects on the emotional and moral toll experienced by healthcare providers, exacerbated by systemic injustice and an unclear pathway to access our ethics committee at the time. We hope this case provides insights for healthcare systems to develop comprehensive strategies to support adolescents boarding in the ER ensuring their dignity.

This chapter introduces linear cryptanalysis from the point of view that historically led to its discovery. This “original” description has the advantage of being concrete, but it is not very effective. However, it raises important questions that motivate later chapters.

This chapter explores the historical, legal, and regulatory landscape of employment testing bias and fairness in Canada. Canada’s history of colonization and immigration has resulted in a multicultural society. In 1984, the landmark Abella Report, and the subsequent Employment Equity Act, established key protections for historically disadvantaged groups, shaping modern employment practices. The chapter discusses the jurisdictional complexities of employment law, detailing federal and provincial regulations that prohibit discrimination based on race, sex/gender, disability, and other characteristics. Legal frameworks (e.g., the Canadian Charter of Rights and Freedoms, the Canadian Human Rights Act, and the Employment Equity Act) define bias and fairness in employment testing. Key court case decisions illustrate legal principles guiding test validity and adverse impact. We also examine professional guidelines, burden of proof requirements, regulatory oversight, and emerging challenges such as AI-driven assessments and balancing validity with diversity. The legal landscape continues to evolve, with growing emphasis on fairness, transparency, and inclusion.