Bart Baesens

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Ethics & AI

📅 October 7th, 2022
🌍 English

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.


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). 

Course Outline

  • Introduction and Context
    • Data Science Ethics
    • FAT Flow: A Data Science Ethics Framework
    • Why Care?
    • The AI Act
  • Ethical Data Gathering
    • Privacy: what data to include or predict, and how?
    • Bias: easily overlooked
    • Human Experimentation
  • Ethical Data Preprocessing
    • “Anonymizing” Data: the ease of re-identification
    • Defining anonymization with k-anonymity
    • Measuring Discrimination
    • Government Backdoors
  • Ethical Modeling
    • Differential Privacy: technique and use cases
    • Including Privacy: from zero knowledge proofs to homomorphic encryption
    • Getting rid of discrimination?
  • Ethical Evaluation
    • Explaining Black Box Models and their predictions: from SHAP to counterfactuals
    • Ethical Reporting
  • Ethical Deployment
    • Deep Fake
    • Unintended Consequences
  • Conclusions
    • Beyond Data Science Ethics
    • Governance
    • Discussion Cases
👩‍🏫 Lecturers

Prof. dr. David Martens
Professor at University of Antwerp

Prof. dr. Bart Baesens
Professor at KU Leuven

🏢 Location

Van der Valk Hotel Brussels Airport (Belgium)

Culliganlaan 4b
1831 Diegem

🏫 Organizer

Bart Baesens

💼 Register

The price is 500 Euro (VAT Exclusive). This includes:

  • a copy of the course material 
  • lunches and coffees

Online registration

Price and Registration

The price is 500 Euro (VAT Exclusive). This includes:

Please register through the link below. After processing your payment, you'll be sent a confirmation e-mail to confirm your registration.

Online registration