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D1.1 User Requirements and Architecture​

D3.1 Multi-dimensional Discrimination Definitions and Operational Measures

D6.1 Data Management Plan​

Periodic Technical and Scientific Report​


TitleAuthorsDate of publicationPublisherLink to publication
Multi-dimensional discrimination in Law and Machine Learning - A comparative overviewArjun Roy, Jan Horstmann, Eirini Ntoutsi12.06.2023ACM FAccT
The Use of AI in school science: a Systematic Literature ReviewDagmar Mercedes Heeg, Lucy Avraamidou05.10.2023Taylor & Francis Group
FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning.Arjun Roy, Christos Koutlis, Symeon Papadopoulos, Eirini Ntoutsi20.10.2023arxiv
Improving the visibility of minorities through network growth interventionsLeonie Neuhäuser, Fariba Karimi, Jan Bachmann, Markus Strohmaier, Michael T Schaub20.05.2023Communications Physics
Studying bias in visual features through the lens of optimal transport.Simone Fabbrizzi, Xuan Zhao, Emmanouil Krasanakis, Symeon Papadopoulos, and Eirini Ntoutsi02.09.2023Data Mining and Knowledge Discovery
Karimi, F., Oliveira, M. On the inadequacy of nominal assortativity for assessing homophily in networks. Sci Rep 13, 21053 (2023)., F., Oliveira, M. 29.11. 2023Scientific Reports
Ureña-Carrion, J., Karimi, F., Iñiguez, G., & Kivelä, M. (2023). Assortative and preferential attachment lead to core-periphery networks. arXiv preprint arXiv:2305.15061.Ureña-Carrion, J., Karimi, F., Iñiguez, G., & Kivelä, M. 22.12.2023PHYSICAL REVIEW RESEARCH


Demo Day: "Ethical and Responsible AI Platforms", Copenhagen Fintech Lab and online

Presentation on BIAS workshop