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Proposing a Roadmap for Designing Non-Discriminatory ML Services: Preliminary Results from a Design Science Research Project

Henrik Kortum-Landwehr; Philipp Fukas; Jonas Rebstadt; Marian Eleks; Marjan Nobakht Galehpardsari; Oliver Thomas
In: Wirtschaftsinformatik Proceedings (2022). Internationale Tagung Wirtschaftsinformatik (WI-2022), February 21-23, Erlangen-Nürnberg, Germany, Springer, 2022.


AI and ML algorithms are being developed with ever higher accuracy. However, the use of ML also has its dark side. In the recent past, examples have repeatedly emerged of ML systems learning discriminatory and even racist or sexist patterns and acting accordingly. As ML systems become an integral part of both private and economic spheres of life, academia and practice must address the question of how non-discriminatory ML algorithms can be developed to benefit everyone. This is where our Research in Progress paper provides a contribution. Building on a case study from the smart living domain and insights from the literature, we quantify the problem of discrimination in face recognition and propose a roadmap for our further research aimed at developing non-discriminatory ML services.