Promotions - Choice's Outstanding Academic Titles 2024 -

Are we conscious or is consciousness simply an illusion? Shadbolt and Hampson examine human consciousness to explore the way artificial intelligence is rapidly progressing toward a day when AI devices may actually be viewed as conscious beings capable of moral and ethical decision-making. When AI devices can no longer be distinguished from interactions with humans, moral and ethical questions arise. One final conclusion of this text is that "Humans have a right to be judged by humans if they so wish," not by artificial intelligences. The academic world is currently flooded with books speculating about the potential horrors or benefits of AI, and many are highly speculative and based on slim premises. As If Human is a very readable, in-depth analysis of the ethics and moral issues of artificial Intelligence and how humans should approach total emersion into the world of AI. This is a book that should be read by the general population and taught in computer science and philosophy courses.Summing Up: Highly recommended. All readers.

In this innovative and powerful text, McStay (Bangor Univ., UK) provides both a thorough description and an epistemic and ethical assessment of contemporary technologies designed to emulate, interpret, and express empathy. As an interdisciplinary professor and director of his university's Emotional AI Lab, McStay demonstrates his mastery of the subject by connecting cutting-edge emotional AI in use today with a multifaceted take on mental integrity, and examines the ethical and social implications of emotional AI on agency, privacy, and autonomy. His approach also brilliantly highlights the risks of oversimplifying emotions, misrepresenting individuals, and using insufficient ethical approaches to address complex ethical dilemmas (like drawing on the trolley problem in the context of self-driving cars). McStay's insights offer a nuanced understanding of humanity's challenges and opportunities in the era of advancing AI technologies, meticulously illustrating where mental integrity considerations (and others) fall short, but also acknowledging current areas of and potential paths to positive progress. Despite feeling somewhat rushed in its final chapters, this text stands out as a pioneering and enlightening piece, meriting reexamination for its profound insights into the intersection of technology, ethics, and human emotion.Summing Up: Essential. All readership levels.

In Critical Data Literacies, Pangrazio (education, Deakin Univ., Australia) and Selwyn (education, Monash Univ., Australia) present an impressive case for why the general public needs data comprehension skills. With relatable, realistic examples, the authors neatly explore data, critical data literacy, and the hopes and fears that data can inspire. They offer grounded strategies to "do data differently" in education, research, activism, and daily life. Instructors in digital or data studies may want to use the entire (under 200 pages of content) text over a semester, though individual chapters, particularly "The Rise of Digital Data in Everyday Life" and "What is Data?" are essential reading for almost any student, regardless of discipline. Critical Data Literacies is an impressive balance of the theoretical and practical at an accessible reading level.Summing Up: Highly recommended. Undergraduates and two-year technical program students.

The editors achieve something greater in this book than the now-familiar model of a high-level overview followed by disparate chapters of data science applications and results. They start with a high-level data science process, and each chapter details how that process is applied. The result is not a guidebook to data science, but instead a guide to doing data science better. The process involves six steps: (1) setting the research goal, (2) retrieving data, (3) data preparation, (4) data exploration, (5) data modeling, and (6) presentation and automation. With each chapter, the authors' motivations for the research as well as their operationalization toward that goal are clear. Chapters describe data sources, including survey and sensor-based approaches. Authors explore multiple models, defining the basis for using them and comparing the resulting outputs. Together, the chapters demonstrate how data science techniques are applied across disciplines and incorporate state-of-the art techniques available at the time of the original research. A caveat: this book has a computer/data science perspective, and the authors gain domain knowledge from literature reviews rather than interdisciplinary collaboration.Summing Up: Highly recommended. Undergraduates through faculty; professionals.

Today, many data science texts focus on helping readers attain proficiency in software tools so they can quickly begin conducting analysis. Such a "learn by doing" approach is useful but often leaves students in need of further basic knowledge about statistical reasoning and how to make choices among the myriad statistical approaches. This text fills the gap by focusing almost entirely on the options for statistical approaches to complex big data problems in various socioeconomic sciences. Anindya Chakrabarti (Indian Institute of Management, Ahmedabad), Bakar (Univ. of Sydney), and Anirban Chakraborti (BML Munjal Univ., India) provide a brief yet dense introductory summary of the emergent properties of complex systems (ant colonies, traffic jams), followed by discussions of particular approaches. The substantive chapters explain commonly used methods employing machine learning, network analysis, and agent-based modeling. Chapter 5 on network theory is especially strong, as it provides compelling examples using Indian business data to illustrate theoretical concepts. Overall, this text will be an excellent resource for gaining understanding of potential analytical methods for readers with existing background and experience, such as active data science researchers and graduate students. It may also benefit graduate and advanced undergraduate students in business and economics with prior knowledge of multivariate analysis, statistical modeling, and clustering methods.Summing Up: Highly recommended. Upper-division undergraduates. Graduate students, faculty, and professionals. Students in two-year technical programs.

This book is a comprehensive, interdisciplinary critique of current perspectives on sustainability and justice in system design. Becker (Univ. of Toronto, Canada) tackles the complexities of technological and societal interactions. He illustrates the impacts of the increasingly opaque information technologies and the rapidly rising amount of computation, which raise significant sustainability questions and have unknown societal implications. Becker proposes changes to the conceptual frameworks of system dynamics and critical system thinking to show nonlinear, counterintuitive effects from minor leverage-point influences. The first part of the book discusses the societal impacts of computing systems, including sustainability, societal debts, and "problemism," a term Becker uses to describe the preoccupation with problem-solving. In the second part, he presents new system design approaches that focus on sustainability and justice in the context of critical systems thinking. Becker discusses value-neutral, rational decision-making concepts and critical engineering requirements from the systemic point of view. Finally, he proposes a reorientation of professional experience and presents professional ethics codes for computing. The book is broadly addressed to readers within computing and neighboring fields. It is a rich discussion of the many alternative perspectives and intellectual practices currently shaping computing.Summing Up: Highly recommended. All readership levels.

Machine Learning, by Jugal Kalita (computer science, Univ. of Colorado, Colorado Springs), is an excellent introduction to Machine Learning (ML) that covers the commonly used ML algorithms in an easy-to-understand fashion. The book is designed as a one-semester textbook for advanced undergraduates and beginning graduate students. Chapter 1 serves as an introduction that provides an overview of the two fundamental types of ML that will be taught: supervised learning with labeled data and unsupervised learning with unlabeled data. Subsequent chapters cover regression, tree-based classification, artificial neural networks, reinforcement learning models, and unsupervised learning in which the algorithms learn patterns exclusively from unlabeled data. Chapter 7 provides concluding remarks and a brief discussion of where to find additional information on ML, particularly on advanced topics like deep learning. This highly readable textbook contains, for each chapter, suitably worked-out programming examples in the R programming language, as well as appropriate exercises.Summing Up: Highly recommended. Upper-division undergraduates, graduate students, faculty, and practitioners.

"Are robots just things? Can they be persons?" (p. 178). Gunkel (communication, Northern Illinois Univ.) provides an in-depth analysis of the wide-ranging discussions of whether artificial intelligence-driven robots will ever attain the moral and legal status and rights of humans. He leaves the reader hanging, advocating for neither the legal nor the moral status of robots but stating that "getting this right will require nothing less than a thorough rethinking of everything [one] thought was right, natural, and beyond question" (p. x). AI and robots will become increasingly sophisticated and will control more of everyday life. However, the future implications are indeterminate and, as Gunkel states, will be both "terrifying and exhilarating" (p. x). This book is well written and has an in-depth discussion of the philosophical and moral implications of AI robots. It would be excellent for students of computer science, robotic engineering, and even courses in philosophy. Extended notes, references, and index sections add to the book's usefulness. The references section, in particular, will benefit students looking for more information on current discussions on AI and robots, and faculty will find a good addition to other classroom resources.Summing Up: Essential. All readers.

In Technical Territories, Munn (research fellow, Univ. of Queensland) reports on his project--funded by the Australian Research Council--titled "Data Centers and the Governance of Labour and Territory." The key idea is that the classical concept of a territory as a geographical area governed by a state needs to be modified in light of communications through cables and the internet. The author examines the proposed new conception of territory by examining three such "technical territories": Singapore, Hong Kong and Christmas Island. Singapore, when viewed in terms of the classical concept of territory, is a small place, but its influence as a technical territory operating through cables and the internet is vast. The same is true of Hong Kong, mainly because of its contiguity with China. Christmas Island has a dual role: it is an island where persons seeking entry to Australia are held when their cases are being processed, and at the same time it is a place which happens to be the terminus of major cables stretching across the Pacific region. This is a very interesting and thought-provoking book of potential interest to any reader inclined to ponder the implications of digital culture for world society, human rights, and governance.Summing Up: Highly recommended. All readers.