Breakfast Event : Algorithmic Fairness

Machines are already used for effective evaluations (e.g. credit scoring or job applications), by detecting patterns in large datasets. However, data often contain biases. As a result, automatic systems can further perpetuate structural inequalities and discriminations. How do we detect those and deal with them transparently when designing algorithms?