Ethics and fairness of Machine Learning applications are becoming increasingly important in the world of Data Science. Widespread use of ML applications, such as recommendation systems or large language models call for methods that ensure that they comply with ethical standards and policies. This workshop offers the chance to experience hands-on how to deal with biased data leading to biased models. We will highlight both the challenges of incorporating fairness into ML applications, as well as the opportunities that modern fairness tools offer in removing bias. This workshop targets ML practitioners and executives alike as incorporating fairness is both a technological as well as a societal challenge.

Objectives

Improve the understanding of the opportunities, challenges and pitfalls when it comes to integrate fairness into machine learning applications