Module 4: Algorithmic fairness in other fields of Machine Learning

Although Algorithmic Fairness has been widely studied in the context of classification problems, there is a growing number of contributions in other fields of Machine Learning such as Regression, Ranking or Word Embeddings between others. In this module, we will provide an overview of the different cases and the proposed techniques to mitigate existing biases.

Readings

Videos:

• Swati Gupta (2019) ACM FAT*: Challenges of incorporating algorithmic fairness in industry

Printed materials:

• Micheal Ekstrand (2019): SIGIR Fairness & Discrimination in Retrieval and recommendation

Case study: Mitigation unfairness