Outstanding Academic Titles 2024: Information and Computer Science

Enjoy this week's Outstanding Academic Titles list snippet: Five titles pertaining to information and computer science

Posted on March 27, 2025 in Outstanding Academic Titles

Enjoy these five selections from the Choice Reviews 2024 Outstanding Academic Titles list. This week, we highlight Outstanding Academic Titles focused on information and computer science. A hearty congratulations to the winning authors, editors, and publishers!

1. Automating empathy: decoding technologies that gauge intimate life
McStay, Andrew. Oxford, 2024

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. View on Amazon

2. Social media and adolescent health
by National Academies, Sciences, Engineering, Medicine

In an increasingly digital society, debates about the impact of social media on adolescent health are among the top discussion items for parents and policy makers alike. Against this backdrop, a group of 11 scholars and accompanying staff were assembled by the National Academies to provide evidence-based answers to “the social media question” and do so in a clear, concise, and compelling manner. Across eight chapters, this volume is among the most lucid and comprehensive discussions of social media published to date. The report reinforces the complexities of social media, and does so in an impartial and dispassionate manner. Reporting is straightforward and focused on the quality and robustness of extant theory and data, and is written in such a way that even those with little or no prior background in research can easily understand. The volume is careful in defining key terms and anchors them in scholarship, and likewise takes appropriate space to elaborate on the “inner workings” of social media platforms. View on Amazon

3. Data science for complex systems
by Anindya S. Chakrabarti, K. Shuvo Bakar, and Anirban Chakraborti Cambridge, 2023

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. View on Amazon

4. Critical data literacies: rethinking data and everyday life by Luci Pangrazio and Neil Selwyn MIT, 2023

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. View on Amazon.

5. Data science: techniques and intelligent applications ed. by Pallavi Vijay Chavan et al Chapman & Hall, 2023

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. View on Amazon.

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