Gleißner, Lea-KathrinBui, MagdalenaKühn, FeyNenninger, AmelieGesellschaft für Informatik2021-12-152021-12-152021978-3-88579-751-7https://dl.gi.de/handle/20.500.12116/37775Algorithms and new technologies help people in several life situations, but society pays a high price for their advantages. Several scandals occurred recently, showing that algorithms are neither neutral nor fair – quite the contrary: They discriminate people as humans do. One approach to create less biased data science projects is the “Data Feminism” method, presented by Catherine D’Ignazio and Lauren F. Klein in their book of the same title. This paper evaluates how feasible the method can be implemented in student projects based on the experiences four Leipzig students made by trying to implement the method into their project ‘Questioning Street Names Leipzig’. The paper focusses on three main concepts: subjective viewpoints and context, crediting all forms of labour, and building and linking communities through public tagging events, thus opening the academic question for some citizen science help. The project utilizes open data and open data sources such as Wikidata and OpenStreetMap. The authors of “Data Feminism” want to encourage students, as well as academic professionals, to think about their bias in their data and to use the data feminism approach to reduce the impact of them and create more ethical computer science projects.endata feminismCitizen ScienceOpenStreetMapWikidataopen datareport of experiences1614-3213