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Ethical AI
Enrollment is Closed

In this course, you learn about Ethical AI. This course is expected to go live by June 2020.
Enrollment is Closed

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 100 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

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

Price

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 Bart@BlueCourses.com. 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).

Requirements

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). You should also have followed and completed our Machine Learning Essentials course.

Course Outline

  • Introduction to Data Science Ethics
    • Instructor
    • Course Outline
    • Why Care about Data Science Ethics
    • FAT: Fair, Accountable and Transparent
    • The FAT Flow Framework
  • Ethical Data Gathering
    • Privacy and Regulations
    • Bias and Experimentation
    • Discussion Cases
  • Ethical Data Preprocessing
    • Input selection
    • k-anonymity
    • Discussion Cases
  • Ethical Modeling
    • Privacy-Preserving Data Mining
    • Removing Bias
    • Comprehensible Models and Explainable AI
    • Discussion Cases
  • Ethical Evaluation
    • Defining KPIs
    • Ethical and Correct Reporting
    • Assessing and Auditing FAT
    • Discussion Cases
  • Ethical Deployment
    • Access to the system
    • Honesty
    • Unintended Consequences
    • Discussion Cases
  • Closing Thoughts and Guidelines
  • Quiz

Prof. Dr. David Martens

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David was born in Wilrijk (Antwerp, Belgium) on September 2, 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”.

Enrollment is Closed