Online college

Algorithmic Fairness

Wat leer je in dit online college?

Welcome to our session on Algorithmic Fairness. This course provides a thorough understanding of bias and fairness in AI systems.

We’ll begin with real-world examples highlighting the consequences of discrimination in AI, and discuss the concept of protected features. Then, we’ll delve into the intricacies of defining and measuring unfairness in algorithms, comparing group vs individual, and causal vs observational fairness.

The course also explores the origins of bias, detailing how it can unintentionally seep into AI systems. We’ll look at data and model design as potential sources and discuss practical strategies to mitigate these biases.

Lastly, we’ll tie fairness to other crucial AI concepts and explore alternative views on fairness, moving beyond standard metrics. We aim to help you create AI systems that not only function efficiently but are also fair and ethical.

Dit online college is onderdeel van de AI Academy

Wil je dit online college en nog vele andere online trainingen op het gebied van AI & ChatGPT volgen?

Dat kan in de online AI Academy. Ontwikkel je kennis en skills op het gebied van AI en ChatGPT met meer dan 100 online leermodules.

Topspreker: Andres Algaba

Andres Algaba is a postdoctoral researcher in Algorithmic Fairness at the Data Analytics Lab at the Vrije Universiteit Brussel (VUB) and an Academic AI Expert at Flanders AI Academy (VAIA).

His research focuses on detecting and mitigating discrimination in algorithmic decision-making. Andres obtained a joint Ph.D. in Business Economics from VUB and UGent and was a lecturer in Econometrics at VUB. He has published his work in AAAI and the International Journal of Forecasting.

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