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Ethics and AI

In this course, you learn about Ethics and AI.

About This Course

Data science has become a technology with many applications, such as in risk management, with counter-terrorism and tax fraud detection applications, or in a business setting to increase profitability and revenues or reduce costs. For a citizen, data science has led to better services and more efficient services. However, just as with any technology, data science has also led to some negative consequences. Ethics is all about what is right and what is wrong. In this course, participants will learn about the different concepts and techniques related to data science ethics.

This course uses a framework to discuss data science ethics in a business setting, evaluating the fairness, accountability and transparency, in the different stages of a data science project: from data gathering to model deployment. There will be ample of case studies and examples that illustrate the importance of considering ethics in data science projects, as well as theoretical concepts and techniques that can be used to improve on the ethical aspects.

The course features more than 3 hours of video lectures, more than 50 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

See Deep Fake to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at:

Note that this course is not available to academic personnel and institutions.


The enrollment fee for this course is EUR 250 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, R/Python scripts, quizzes and certificate).


Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing). It is also recommended to follow our BlueCourses Machine Learning Essentials course.

Course Outline

  • Introduction
    • Instructor Team
    • Publications
    • Course Outline
    • Disclaimer
  • Setting the stage
    • Data Science Ethics
    • Trolley Problem
    • Data, Algorithms and Models
    • FAT Flow: A Data Science Ethics Framework
    • Why Care?
    • Subjectivity of Ethics
    • Quiz
  • Ethical Data Gathering
    • Privacy
    • GDPR
    • Thought Experiment
    • Bias
    • Bias in our language
    • Experimentation
  • Ethical Data Preprocessing
    • Ethical Data Preprocessing
    • Anonymizing Data
    • Issues with k-anonymity
    • On-Line Re-Identification
    • Proxies for Discrimination
    • Measuring Discrimination
    • Data Preprocessing for Non-Discrimination
    • Massaging
    • Measuring Fairness Revisited
    • COMPAS Case
    • Government Backdoors
    • Quiz
  • Ethical Modeling
    • Differential Privacy
    • Differential Privacy in Action
    • Differential Privacy Variants
    • Including Privacy
    • Zero Knowledge Proofs
    • Homomorphic Encryption
    • Discrimination
    • Quiz
  • Ethical Evaluation
    • Explain
    • Trust
    • Trust: Lab Setting versus Real-Life
    • Compliance
    • Improve
    • Comprehensible and Explaining
    • Explanations
    • Instance-based Explanations
    • Advantages
    • Ethical Reporting
    • p-Hacking
    • Multiple Comparisons
    • What to Report
    • Quiz
  • Ethical Deployment
    • Access to System
    • Deep Fake
    • Unintended Consequences
    • Solutions
    • Cautionary Tales
    • Quiz
  • Conclusions
    • Beyond Data Science Ethics
    • Discussion Case
    • Conclusions

Prof. Dr. David Martens

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David was born in Antwerp (Belgium) in the Fall of 1979. He lives with his wife Jasmien and son Stan in Antwerp. He enjoys traveling, and is a big fan of New York, where he lived for 18 months spread over several time periods.

David is professor at the University of Antwerp (Belgium). He is co-founder of spinoff Predicube, a company that works on technology to leverage online publishers’ data assets for improved advertising. His PhD work was on the use of comprehensible classification models for credit scoring, for which he was finalist for the prestigious SIG-KDD doctoral dissertation award. His work on comprehensible models, explainable AI and behavioral data mining has been published in high-impact journals such as Machine Learning, IEEE Transactions on Knowledge and Data Engineering, MIS Quarterly and Information Systems Research. David is author of the forthcoming book on “Data Science Ethics”.