Algorithms, and the data they process, play an increasingly important role in decisions with significant consequences for human welfare. While algorithmic decision-making processes have the potential to lead to fairer and more objective decisions, emerging research suggests that they can also lead to unequal and unfair treatments and outcomes for certain groups or individuals.
This Summer School is an attempt to engage multi-disciplinary teams of researchers and practitioners to explore the social and technical dimensions of bias, discrimination and fairness in machine learning and algorithm design. The course focuses specifically (although not exclusively) on gender, race and socioeconomic based bias and data-driven predictive models leading to decisions.
The summer school will be filmed and will be available as a MOOC late 2019.
A basic understanding of machine learning is strongly recommended
