Assessing Gender Bias in Predictive Algorithms using eXplainable AI
Cristina Manresa-Yee, Silvia Ramis · 2021
Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial).