Promote an informed reflection on human labeling bias and how this bias affects and influences the datasets that are fed by humans into the various machine learning algorithms. – (“biased data in, biased data out”).

Present, raise awareness and print transparency about some examples of “human labeling bias” (ImageNet and worldNet) and understand what are the implications of these in the propagation of social injustices (Stereotypes, prejudices, gender and ethnic discrimination, etc…).