Abstract: 

Recently, academics as well as policy makers have written many papers, on responsible data science / AI. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed.

Session Outline
1. The session starts with an overview of examples of data science applications that are considered unfair / unethical, as well as the main `driving sources’.
2. Hereafter, an overview of proposed policies and frameworks, as well as upcoming regulation is provided.
3. Next, the discussion will concentrate on `fairness’. An overview of the (academic) literature will be provided including an in-depth discussion of the similarities and dissimilarities between different approaches. The concepts will be illustrated by an application of open-source Python packages that provide so-called `bias-dashboards’. An open-source dataset will be used throughout. An overview of methods that try to enforce fairness by design is provided. Again, all concepts will be illustrated by a selection of open-source packages.
4. The session will be concluded by a discussion of the framework that de Volksbank (a Dutch retail bank) has developed for its data science activities.

Background Knowledge
Python (Jupyter notebook)

The session focuses on concepts and not on technical implementation. Mathematics will be used in order to provide clear definitions. The notebooks will be extremely easy to use -- they just serve as an illustration. The discussion of algorithms/models that try to enforce fairness-by-design via in-processing requires that attendees understand the core (supervised) learning concepts.

Bio: 

Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journals in econometrics and statistics. Ramon has taught courses in data science, econometrics, life insurance, machine learning, mathematics, probability theory, quantitative finance, and statistics at Tilburg University, Tias business school, Tilburg Professional Learning, the Jheronimus Academy for Data Science (JADS), and the Dutch Actuarial Institute. In his work at de Volksbank, Ramon mainly works on projects related to data-driven innovation, but also on governance aspects like frameworks for responsible AI & data science and the use of techniques for privacy-preserving data analytics.