Bringing AI Fairness into classrooms

AI has become an integral part of our daily lives, influencing various systems that often make impactful decisions affecting human lives. Despite aspirations for "fairer" and "more neutral" outcomes, the responsibility of weighing ethical considerations and objectives still rests upon us humans. Therefore, it is crucial to introduce future developers and decision-makers, both teachers and students alike, to normative questions from an early stage.

A case study is used to illustrate and discuss key normative considerations in the course of AI development. The final design decisions are made by the participants themselves in an interactive Python notebook. The result can therefore differ considerably between the participants, which forms the basis for a final discussion.

Learning objectives:

• Basic understanding of a prototypical fair ML lifecycle applied to a case study
• Practical understanding of how fairness objectives can be implemented in AI
• Key insights regarding the fallacies, challenges, and tradeoffs surrounding algorithmic fairness
• Reflection of the normative motivations guiding the technical tradeoffs

What do I need in order to use it?

• As a teacher, you should have a basic understanding of python programming, ML training, and fairness evaluation.
• The experiment involves an interactive Jupyter notebook which requires a setup (see setup slides)

Copyright / license

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY-NC-SA 4.0).

As such:

You are free to:

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• Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
• NonCommercial — You may not use the material for commercial purposes.
• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Authors, citation

Luca Deck, University of Bayreuth, Germany

Cite as:
FairBank: An interactive python notebook to experience the normative challenges and tradeoffs of fair AI development, licensed under CC-BY-NC-SA, via https://github.com/AI-for-Business/FairBank

DOI - code

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• You can find all of our repositories here.
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