Understand what are Unfair/Unethical through examples of applications
Explore Policies & Frameworks for fairness
How to use ppen-source approaches and techniques for fairness
Understand practical example of fairness in retail banking.
Ramon van den Akker, PhD
Principal Data Scientist; Associate Professor, Econometrics| de Volksbank; Tilburg University
Module 1: Unfair/Unethical Examples of Applications
- Examples of data science applications that are considered unfair / unethical
- Main `driving sources’ behind such applications
Module 2: Policies & Frameworks
- Proposed policies and frameworks
- Upcoming regulations across the paradigm
Module 3: Open-source approaches and techniques for fairness
- Overview of the (academic) literature
- In-depth discussion of the similarities and dissimilarities between different approaches
- Application of open-source Python packages that provide so-called `bias-dashboards’.
- Using open-source datasets and packages for demonstration
- Overview of methods that try to enforce fairness by design.
Module 4: Framework used by de Volksbank (a Dutch retail bank)
This course is for current or aspiring Data Scientists, Machine Learning Engineers, AI Product Managers
Knowledge of following tools and concepts is useful:
Python (Jupyter notebook) and supervised ML concepts.
The session focuses on concepts and not on technical implementation.
Mathematics for data science will be used in order to provide clear definitions.