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comes to its decisions over a large dataset (global explanation). Therefore, a set of examples in the chapter highlights the need for explainable and comprehensible model predictions in order to avoid skepticism and reluctance to use the model to make a real decision, and to identify errors in the model itself and provide further directions to improve its performance. Chapter 5 emphasizes the importance of an ethical evaluation of the model. It stimulates the use of appropriate measures of predictive performance (e.g., showing misclassification rates as well as accuracy metrics), fairness (e.g., assessing the privacy of the dataset and reporting transparently the involved sensitive groups) and measures related to what extent an explanation of the model can be provided. The author’s complaints of unethical use of data (data dredging) and interpretation of the results (p-value hacking, missed multiple comparisons), suggests that researchers should report transparently (good and bad) outcomes and ensure reproducibility. With cautionary tales and discussion points the author makes one reflect on ethical conduct in line with the principles of research integrity. The last stage is the model deployment which is not exempted from ethical concerns, and some of these are discussed in Chapter 6. This part of the book draws the reader’s attention to the following issues: the access to the data science system can be, for various reasons, limited and this constraint can give power to those who have access; the predictions generated by the model may provide different treatments to people; models may be vulnerable and lend themselves to dishonest use thereby affecting negatively people and society. Such aspects highlight the need for a data science ethics policy and the advisability of creating an ad hoc committee to ensure its implementation. The author composed the book as a sequence of questions, raised by the continuing need to derive knowledge from data while respecting its protection, and as a set of techniques and measures in response to them. Nevertheless, the book is not a cookbook on what to do to be ethically correct in each step of a data science practice; it reveals the ethical implications in the data science applications and proposes a range of solutions highlighting their merits and risks, in the ongoing search for a balance between the practical utility of data analysis and in compliance with ethically right choices. As a matter of fact, ethical data science is not a checklist, but it is a way of thinking and acting when working on data. The book highlights the author’s ability to draw on ethical concerns through real-life examples. In fact, the most striking ones set precedents that often caused the milestone change of company and governmental choices in the direction of an ethical practice of data science. Moreover, in all chapters, ethical concerns are introduced by an opening story, and the underlying concepts are presented by immersing the reader in existing cases linked to world-famous company names, but also to simple people in which the reader can identify with. The book does not outline in-depth technical, mathematical, and computational details, but the bibliographical references are timely and give a fairly complete overview of the measures and techniques currently on offer. Furthermore, the legal references in European and other legislation, highlight regulatory developments. The book is suitable for students in data science and business, data scientists who transform data into knowledge and innovation, or managers who derive business and competitiveness from data. Yet, this is a book aimed at all those who want researchers, companies and governments to be ethically responsible when making decisions using their own data and for purposes that might involve them. Each of us can become a data subject and a model subject, thus, each of us should read this book to become aware of what ethical thinking should orient data-driven decisions, which affect us much more closely than we might expect.