Organized by ZHAW and ethix
The 21st century is shaped by the ever-increasing use of data for getting new insights and making better decisions. The center of such applications are data-based prediction models. More often than not, these systems do produce unintended discrimination and social injustice, a phenomenon which has been called “algorithmic bias” or “algorithmic fairness”. Newly built tools and the ones already in place today are both affected. This has triggered European Union lawmakers to develop and publish a proposal for a Regulation on Artificial Intelligence (AI) earlier this year, which requires algorithmic systems to avoid such biases. In addition to that, there is an increasing awareness of society and customers regarding the social implications of data-based systems. More and more, these systems are expected to be fair, non-biased, and non-discriminatory.
However, in practice, it is not clear how to create fair algorithms and how to ensure that data-based prediction and decision models fulfill clearly defined fairness requirements. In this workshop for practitioners and data scientists, you will learn how to build fair prediction-based algorithms, and how fairness issues are to be included in predictive modeling.
Lessons to be learned:
In this hands-on workshop:
- You will learn how to combine data-based prediction models with fairness requirements.
- You will learn how algorithmic (un)fairness is defined and measured in a practical context.
- You will learn how to construct fair decision algorithms while still harvesting the benefit of a good prediction model. This is based on a recently developed methodology.
- You will apply the methodology to concrete use cases and examples.
Target audience
Our workshop is mainly intended for data scientists. However, deep technical knowledge is not required. Therefore, we are happy to welcome a broad audience with different backgrounds from both industry and academia.
