Applied Philosophy of Science and Data Ethics
2021 - 2025: Last compiled: 2025-07-24
Preface
This class book gathers the contents addressed in the course Applied Philosophy of Science and Data Ethics from the Master of Data Science at the University of Luxembourg (UL). This course will introduce basic philosophical and scientific concepts supported by examples and discussion. The course expects pro-active participation from the students in the form of presentations and essays as well as open debates.
Course presentation
As much as data science involves automating tasks, we should avoid falling for the automatization of the mental processes we undergo to solve new problems. We data scientists are used to dealing with data as much as we do with the methods used to process such data. Still, rarely, if ever, we stop to question the methodologies that justify employing one method or another. Surprisingly for many, data science is as much about science as about data. We have refined the data acquisition tools and technology stacks used for accomplishing all tasks covered under the data science umbrella. However, below the layers of tools and frameworks that facilitate our daily work, there lies a foundational ground of assumptions, principles, and constraints that shape the way we do data science.
During the last few years, I have taught the students of the Master of Data Science to question such foundations and the reasons behind many tools’ weaknesses and strengths. In this course, we delve into philosophical issues, such as Hume’s problem of induction, and link them with the recent challenges of artificial intelligence (AI) in the domains of my working experience, like healthcare, systems biology, and computer networks.
We tackle scientific explanation, causation, and association, learning the strengths of Hempel’s covering-law model and how it fails to generalize once causality enters into play. We discuss the issues of empiricism with causality and showcase the power of causal inference in epidemiology. This leads us to the role of biases, confounders, and surrogate attributes in AI performance. The core of the course is the scientific method, we jump from Popper’s attempt to make science purely deductive to the risks of confirmation, and how diversity plays a crucial role in shaping the quality of datasets. The students get acquainted with the power of randomized controlled trials and learn to relate the strengths of randomization and stratification to current AI techniques such as cross-validation. This is accompanied by historical cases from scientists and doctors like Semmelweis, Fisher, Yersin, et al. They showed us how to properly set scientific hypotheses and how experimental control can play both in favor and against us. This leads to the final part of the course, data ethics, where we learn how cases like the Tuskegee tragedy shaped current regulations for informed consent, and how we can turn ethical values into actions to prevent our solutions from discriminating or wrongly modeling the phenomena at issue.
I believe this course should be taught in every computer science faculty. It requires constant updates to follow the trending topics. One year is COVID-19 and the misleading statistics of vaccine effectiveness put by anti-vax supporters; the next year is the issues of social media applications among teenagers; and today the consequences of generative AI. However changing the present may be, past theories provide a strong base material for this course that students learn to appreciate. The topics learned in this course will remain useful independently of technological advancements. They are essential knowledge for future data scientists to face the upcoming challenges and build the solutions to overcome them.
Course at UL
Most of the contents of this book are taught in the course “Applied Philosophy of Science and Data Ethics” in the Master of Data Science at the University of Luxembourg (UL). The course goal is to provide the students with guidelines and methodologies to identify epistemic and ethical issues present in data science. We expect the students to develop a critical eye that helps them mitigate such problems in their daily work as data scientists.
During this course, students will learn by example different layers of the scientific method and how they relate to data science and data ethics. In particular, they will learn how the mechanisms behind the data affect the data analysis, and how the different types of scientific inference condition method choice and affect the conclusions drawn from the analysis. In this sense, examples of statistical abuse, misconduct and bad visualization will be shown together with their, sometimes catastrophic, collateral consequences.
Target audience
Before we describe the course topics and objectives, we must acknowledge the characteristics of the students who register for the Master of Data Science at the University of Luxembourg as they will shape the course contents and structure. The study program receives students from the European Union (EU) and abroad, adding several dimensions of diversity, such as native languages, study frameworks, and cultural differences, which increase the richness of the class debates but also showcase differences in the student’s background knowledge. Of course, we have students from neighboring countries like Germany, France, or Belgium who are also part of the Bologna zone, ensuring comparability in the standards and quality of higher education qualifications. Students from other continents (e.g., Asia, Africa, and South America) can sometimes constitute most enrolled students depending on the year. On top of these differences, students come from different bachelor backgrounds (e.g., computer science, statistics, biology, physics), and some have had professional experience in their fields. The Master in Data Science is a program from the Department of Mathematics, with half of the courses in the first two semesters consisting of mathematics courses. By way of example, one key difference that can be appreciated during the course in students both from the EU and abroad is their knowledge regarding propositional logic, which is usually taught (if at all) in high school curriculums.
Course objectives
In line with the European Qualitity Framework, Bachelor degrees require a critical understanding of theories and principles, while Master degrees involve higher specialised knowledge and critical awareness of knowledge issues in a field. In this case, the field at issue is data science and the contents will tackle philosophical and ethical issues concerning data science.
Yet, the aim of this course must not be regarded as offering a complete course on the philosophy of science and data ethics but rather as a leveling course to acquaint the students with the concepts of the philosophy of science and how they relate to the challenges of data science and the current ethical debates of the information era. Importantly, as Dr Grüne-Yanoff highlights, teaching the evolution of scientific methods is pedagogical, especially if done through strategies of hyperbole, skepticism, and discussing historical scientific and ethical errors (Grüne-Yanoff, 2014).
The course objectives include:
Critically appraise the goals of science, so students can distinguish prediction, explanation, understanding, and design, and grasp how different philosophical views shape what counts as scientific success.
Justify methodological choices, because data science involves implicit assumptions and conventions, and students must be able to explain their decisions and recognize how methods evolve over time.
Understand scientific inference, to differentiate between deductive, inductive, and abductive reasoning and relate these to reasoning strategies used in both science and AI systems.
Grasp the epistemology of uncertainty, to understand how science moves from data to belief and why traditional models like the Hypothetico-Deductive method fall short in data science, where probabilistic reasoning, model comparison, and frameworks like Bayesian and frequentist inference better capture uncertainty.
Acknowledge the limits of data, so students appreciate the role of domain knowledge, assumptions, and the iterative nature of inquiry when moving from data to generalizations.
Discriminate between correlation and causation, as confusing the two remains a widespread and harmful error in data-driven fields.
Recognize and mitigate biases, because biases, whether from measurement, sampling, confounding, or algorithms, can distort every stage of the data pipeline.
Understand the role and limits of models and experiments, since modeling, experimental control, and design decisions shape what can be inferred, especially regarding causality and generalizability.
Integrate non-epistemic values, because ethical, legal, and social considerations directly impact how data science is practiced and evaluated in real-world contexts.
Connect concepts, as a meta-goal students should relate philosophical, methodological, and ethical dimensions into a coherent understanding of data science practice.
The justification of these objectives derives from the work these future data scientists will do. Data science practice is driven by legal, ethical, and resource pressures, so students must see how these forces shape their methods (Obj 9-10) and where each tool reaches its limits (Obj 2; Obj 8). Spotting bias, spurious links, and causal traps in messy data (Obj 6-7) depends on a strong understanding of science’s aims, the logic of inference, and the role of domain knowledge (Obj 1; Obj 3-5). The key takeaway message from this course is to question the data science methods, acknowledge that data without domain knowledge is insufficient, and recognize the current ethical debates and how they guide and constrain the data science developments of our time.
Course structure
The course is part of the first semester of a two-year master’s program. It consists of five chapters covered over 14 weeks, with a 90-minute class each week. These sessions are supplemented by weekly online quizzes, readings, and essay assignments. Depending on the pacing and in-class participation, topics may shift between weeks. To address potential scheduling issues, some video lectures cover topics in detail and can replace in-class lectures if needed to allow more time for discussions, exams and debates. These videos also include foundational content, such as an introduction to basic concepts of propositional logic in Chapter 2.
The current chapters and the order they are taught in class are:
- Chapter 1: Scientific Goals, Methods, and Knowledge (Week 1)
- Chapter 2: Scientific Inference (Weeks 2-6)
- Chapter 3: Empirical Practices and Models (Weeks 7-9)
- Chapter 4: Experimental Control and Statistical Abuse (Weeks 10-12)
- Chapter 5: Ethics and Responsibility (Weeks 13-14)
About this course
Despite deep historical ties between philosophy and computer science, especially in areas such as logic, the two disciplines have been largely taken apart largely in recent decades. Computer science curricula have increasingly focused on technical and practical applications, such as programming, algorithms, and systems design, while philosophy courses tackling theoretical inquiry and critical thinking have been relegated to other departments. This separation has diminished opportunities for students to explore foundational questions about the nature of computation, the ethics of technological innovation, and the philosophical implications of artificial intelligence. Below, a selection of courses and resources is available as guide for future instructors in the philosophy of science for data science. Moreover, the rest of the paper is accompained with examples and references related to the course content which further extend the materials listed below.
While optional and online courses in topics like philosophy of science and data ethics are available, these subjects are not typically integrated into the core courses of computer science departments. Philosophy of science, in particular, is rarely positioned as a fundamental component of computer science education. This gap calls for better interdisciplinary in computer science programs to bridge technical skills with philosophical inquiry.
Some of these online courses which align with the philosophy of science and data ethics and which have influenced this course, are described below.
The first chapters of this course are inspired by the book from Prof. Dr Lars-Göran Johansson (Johansson et al. 2016) “Philosophy of science for scientists”. For several years, Prof. Dr. Till Grüne-Yanoff taught an online course (now unavailable) titled “Philosophy of Science for Engineers and Scientists” on the edX platform, which combined theoretical concepts with practical examples. Currently, he teaches courses on the methodology of science at KTH Royal Institute of Technology in Stockholm, Sweden. He advocates for the revision of the traditional curriculum of Philosophy of Science to better align with modern scientific disciplines (Grüne-Yanoff 2014). Regarding the second part of the course, which covers data ethics, I would like to thank the Univerisity of Michigan, for their online courses (from which I was already a fan during my PhD). Especially, Prof. Dr. H. V. Jagadish’s Data Science Ethics course, offered on Coursera by the University of Michigan, which has influenced this course’s data ethics content. The University of Oxford’s Department for Continuing Education provides short courses on Philosophy of Science and Data Ethics, which offer valuable opportunities for expanding knowledge in these areas.
However, the integration of Philosophy of Science into computer science curricula remains limited and the author has struggled to find dedicated resources and courses. Some exceptions exist, such as the University of Bayreuth in Germany, which offers a Master’s program in Philosophy & Computer Science, and the University of Oxford, with an undergraduate program in Computer Science and Philosophy. More recently, the University of Cordoba in Spain will debut a new bachelor in Mathematics and Philosophy in 2025. These programs highlight the potential for interdisciplinary approaches, though they remain rare in broader computer science education.
Additionally, a great variety of books are available and many have shaped the course materials, including: The Book of Why (Pearl and Mackenzie 2018), Ethics and Data Science (Loukides, Mason, and Patil 2018), Understanding philosophy of science (Ladyman 2012), Philosophy of Natural Science (Hempel 1966). Again, one key work is Philosophy of Science for Scientists (Johansson et al. 2016), which emphasizes that lessons from natural sciences are relevant to students and researchers in social and human sciences, to which I must add data scientists. Other books aimed toward the general public such as Philosophy of science: A very short introduction (Okasha 2016) or Philosophy of science : a new introduction (Barker and Kitcher 2014) offer a variety of examples to illustrate the topics of Philosophy of Science. Some more have influenced examples and ethics lessons such as How charts lie (Cairo 2019) and Automating inequality (Eubanks 2018).
Since 2021, the author regularly updates this online class book that suits both students and instructors with topics taught in class and additional material.
I hope any resemblance or imitation is seen as an act of flattery.
Roadmap for instructors
As a brief roadmap for instructors without formal training in philosophy, this author believes (Okasha 2016) and (Pearl and Mackenzie 2018) (chapters 1 to 5) offer an accessible way into core issues of Philosophy of Science and causal inference. This can be complemented with (Johansson et al. 2016) (chapters 2, 3, 4 and 13) and (Ladyman 2012) (chapter 2) as reference books to understand and link relevant concepts. Though, the most important task is to connect such issues with the instructor expertise in data science to enahnce the applied aspect of the course.
Disclaimer
Although the impact and extension of the topics addressed in this class book (and the course) are broad and diverse, its length is limited. Hence the scope and depth of the contents are restricted. Consequently, several topics on Philosophy of Science are tackled superficially while some others are completely ignored. Such philosophical questions are handled from a practical data science point of view. Similarly, Data Ethics is a relatively new matter in continuous evolution. Therefore we will try to cope with the main issues in the most practical way.
About this class book
This class book was made thanks to the great tutorial available on the book “Open tools for writing open interactive textbooks (and more)”.
License
Licensed under CC BY-NC-ND 4.0
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