Towards a gendered innovation in AI

In this paper we address the problem of including the gender dimension in the content of Computer Science, notably in Artificial Intelligence (AI). We analyze first the fairness of Machine Learning (ML) algorithms from a gender point of view. Due to their nature of being bottom-up data-driven algorithms, the most common biases diffused in society about gender and ethnicity can be captured, subsumed and reinforced by them, as many ML applications show. Then, to understand how to develop a new gendered (Computer) Science and promote a gendered innovation in AI, we show a formal reflection on the scientific method utilized to produce innovation and a critical analysis of the logical rules underlying it.