TY - UNPD A1 - Bauer, Kevin A1 - Pfeuffer, Nicolas A1 - Abdel-Karim, Benjamin M. A1 - Hinz, Oliver A1 - Kosfeld, Michael T1 - The terminator of social welfare? : The economic consequences of algorithmic discrimination T2 - SAFE working paper series ; No. 287 N2 - Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time. T3 - SAFE working paper - 287 KW - Algorithmic Discrimination KW - Artificial Intelligence KW - Game Theory KW - Economics KW - Batch Learning Y1 - 2020 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/55235 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-552357 PB - SAFE CY - Frankfurt am Main ER -