Artificial Intelligence systems increasingly match or surpass human performance across a wide range of cognitive tasks. Recent advances in large language models have yielded conversational abilities often indistinguishable from human dialogue. In this chapter, we argue that whether artificial agents should be considered empathetic depends on how empathy itself is defined and how it relates to subjective experience. We show that different philosophical schools produce distinct, and often incompatible, answers to the question of whether such a system should be considered genuinely empathetic. On one hand, empathy is a functional trait that can be fully characterized by observable behavior; on the other hand, empathy is inseparable from subjective experience and conscious feeling. Drawing on the philosophical notion of the “zombie” we introduce a thought experiment involving a hypothetical chatbot that exhibits perfect empathic behavior across all conceivable benchmarks while lacking any subjective experience. This framing allows us to disentangle functional performance from conceptual attribution and to examine whether empathy judgments depend on observable behavior alone or on assumptions about inner experience. As artificial agents become increasingly integrated into emotionally contexts, understanding how and why humans attribute empathy to machines becomes a pressing scientific, ethical, and societal question.

Chapter 5 analyses how to design more trustworthy and safer Clever Biometric Computing systems, that is, sensor technologies that read and computationally interpret the human body using AI. The complex socio-technical histories around facial recognition technologies (FRT) and emotion recognition (ERT) are presented, as key examples of Clever Biometric Computing. Discussion focuses on why they are ethically, legally, and socio-technically problematic. The chapter then explores the EU policy landscape around trustworthy AI and the EU Artificial Intelligence Act (AIA). The AIA formal legal requirements for design of AI systems are comprehensively analysed, particularly the turn to design for regulating high-risk AI systems (HRAIS). Some requirements that are analysed include human oversight, fundamental rights impact assessments, data governance, record keeping, and transparency. Different examples are presented too, such as ERT for safety in cars, industrial workplaces, and law enforcement use of live FRT. Ways to overcome challenges of operationalising the HRAIS requirements from an Accountable Design perspective are a key part of this chapter.

In this introduction, we lay out the conceptual and structural foundations of the book. We define machine learning for readers unfamiliar with the field, covering its main types (supervised, unsupervised, reinforcement, and generative learning) and the key stages of the ML pipeline: datasets, features, model training, testing, and output. We then examine what "human-centered" means in computing, tracing how the concept has expanded beyond usability to encompass well-being, social impact, and the needs of communities beyond individual users. We define human-centered ML as the application of a human-centered mindset to the design, use, and evaluation of ML, and then map out the book’s four parts: ML in the arts and humanities; the methods and practices of human-centered ML; humans in the ML process; and ML in its broader social context. It closes with perspectives from the book’s interviewees on what human-centered ML means to each of them: definitions that range from keeping humans in the loop throughout the entire pipeline to building ML from within affected communities to a critique of the "human-centered" framing as too narrow for a world in environmental crisis.

This chapter introduces readers to the basics of AI ethics. It reviews core topics such as moral agents, different ethics frameworks, and how ethics are considered in, by, and for the design and deployment of AI systems.

This chapter reviews key guidelines, standards, and regulations related to AI at the time of writing. It provides practical and theoretical advice on how to build an effective AI governance framework in organizations by considering the plethora of different policies, stakeholders, and values.

This chapter examines the ethical considerations shaping Behavioural Data Science prior to the implementation of the European Union’s Artificial Intelligence Act. While recent regulatory developments mark a significant shift in legal obligations, many of the ethical challenges addressed here remain foundational and continue to inform practice today. Artificial intelligence and data science offer powerful tools for accelerating discovery across disciplines by enabling large-scale data analysis, uncovering behavioural patterns and revealing societal trends previously inaccessible to researchers. Yet the increasing reliance on data-driven methodologies introduces complex ethical risks – to individuals, communities and the broader social fabric – especially when behavioural insights are extracted from sensitive or repurposed data. This chapter traces how research communities and policy-makers, prior to the AI Act, developed and operationalised ethical data governance principles through various mechanisms. Among these, data trusts emerge as a particularly promising model. Situated between open access and rigid institutional control, data trusts provide a flexible framework for balancing the imperative to share behavioural data with the need to protect rights and foster public trust. The chapter explores the conceptual grounding and practical deployment of data trusts, arguing that, while formulated pre-AI Act, they remain highly relevant for ensuring legitimacy, accountability and transparency in behavioural data governance today.

Technology has become the ‘fourth party’ in dispute resolution through the growing field of online dispute resolution (ODR), which includes using a broad spectrum of technologies in negotiation, mediation, arbitration, and everything in between. Furthermore, AI has become a particularly powerful fourth party, and may even become the third party in some situations where AI makes the decision. Accordingly, it is imperative that professionals and policymakers tread cautiously and remain responsible in their use of AI in dispute prevention and resolution. This chapter will discuss foundational considerations around the benefits and risks of AI in dispute resolution, and the regulations as well as ethical guidelines that should remain a top priority when using AI in civil dispute resolution.

Ethics are of utmost importance when doing research and later writing about it. There are many ways to get tripped up ethically in both research and writing. This chapter summarizes some of the pitfalls and gives you advice as to how to avoid them.

Chapter 2 is devoted to AI ethics, broadly defined. It provides an overview of ethical, responsible, safe, trustworthy, transparent and explainable, accurate, just and fair, accountable, sustainable, robust, accessible and inclusive AI. Just as the definition of AI itself is fraught with disagreement, words with a connotation of “good” AI have generated considerable controversy among academics, social movement activists, journalists, business leaders, and lawmakers. This chapter aims to represent the plurality of positions. Furthermore, the adjectives associated with getting AI right are mutually supportive, but tensions between desirable goals are mentioned as well.

A comprehensive chapter exploring AI's role in education, with insights into democratic, ethical, and social reflections, different views on technology in schools, and an analysis model for AI as a resource in school, including ethical reflections and management. The chapter gives room especially for global educational guidelines and presents guidelines and standards for how AI should be implemented and used responsibly in educational environments.

While generative AI enables the creation of diverse content, including images, videos, text, and music, it also raises significant ethical and societal concerns, such as bias, transparency, accountability, and privacy. Therefore, it is crucial to ensure that AI systems are both trustworthy and fair, optimising their benefits while minimising potential harm. To explore the importance of fostering trustworthiness in the development of generative AI, this chapter delves into the ethical implications of AI-generated content, the challenges posed by bias and discrimination, and the importance of transparency and accountability in AI development. It proposes six guiding principles for creating ethical, safe, and trustworthy AI systems. Furthermore, legal perspectives are examined to highlight how regulations can shape responsible generative AI development. Ultimately, the chapter underscores the need for responsible innovation that balances technological advancement with societal values, preparing us to navigate future challenges in the evolving AI landscape.