Abstarct: Aabstract In this thesis the following question is considered : given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? The issues of direct and indirect discrimination are addressed, the latter resulting from implicit learning of protected characteristics from nonprotected ones. Rigorous mathematical definitions for direct and indirect discrimination, are provided. Also a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination is introduced. This formula works in any statistical model. Its application on a health insurance example, using a state-ofthe- art generalized linear model and a neural network regression model is demonstrated. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.