There are numerous examples of how the datasets that are used to train the algorithms that rule our daily lives are biased. For instance, tools that automatically translate professional titles tends to follow gender stereotypes: males are doctors while nurses are females. There is also bias against faces of females and faces of people of colour. But if these biases are … Continue reading The handful of datasets that rule our lives