Nowadays, Machine learning (ML) is used to predict nearly everything: from houses price to future criminals.
Often, decisions based on ML systems have a significant impact on our life: they influence recruiters who evaluate our CV or banks that should grant us a loan.
However, can we trust these systems and be sure that their predictions are fairly computed?
What if a bias in the training data is amplified by these systems leading to unequal decisions?
In the talk we will understand how to identify such biases and mitigate their effects.