Algorithmic Discrimination

Algorithms are now playing a major role in many areas of modern life. Many companies rely on classification algorithms to segment markets and to target these segments more accurately with appropriate marketing measures. In the often generalized mechanical classification of individuals, there are always moments of prejudice (Sweeney 2013). The judgment of the European Court of Justice from the year 2011 and the economic consequences exemplify that the regulation of a possible algorithmic discrimination (AD) is a special challenge for the policy. Binding gender neutrality in the collection of health insurance data has induced a price increase for male clients, while the price for female clients has stayed the same, rather than decreasing, which may result in a macroeconomic welfare loss (Sass and Seifried 2014).Therefore, we plan to examine financial and insurance products for the occurrence and extent of AD and to derive appropriate policy recommendations for policy and economics. The research relevance arises from the problem, that in case of inadequate or inappropriate intervention AD can lead to a lack of transparency, an increasing problem of trust and ultimately to market failure (Colangelo 2017).