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May Newsletter

The official newsletter of MAMMOth Project [MAY2023]

Call for Papers is out now!

Fairness in Machine Learning continues to be a growing area of research and is perhaps now more relevant than ever, as new AI-powered applications like ChatGPT and MidJourney are being widely used by the public, and legal regulations of AI/ML (e.g., the EU AI Act) are close to being adopted.

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Initial studies on fair ML and AI bias focused on the technical aspects of discriminatory algorithms and treating fairness as an objective to be optimized. However, recent work recognizes the importance of taking a broader perspective that includes legal and societal implications and involving various stakeholders in designing fair algorithms.

• Workshop at ECML PKDD 2023, 18th or 22nd of September (TBA), Torino ??

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There are currently 3 submitted and 1 accepted paper

Krasanakis, E., & Papadopoulos, S. (2023). Graph Neural Network Surrogates of Fair Graph Filtering. arXiv preprint arXiv:2303.08157. Available at:

Roy, A., Horstmann, J., & Ntoutsi, E. (2023). Multi-dimensional discrimination in Law and Machine Learning-A comparative overview. Accepted in ACM FAccT. 2023 Available at:

Sarridis, I., Koutlis, C., Papadopoulos, S., & Diou, C. (2023). FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations. arXiv preprint arXiv:2304.14252. Available at:

Roy, A, Koutlis, C., Papadopoulos, S., & Ntoutsi, E. (2023). FairBranch: Fair multi-task learning using network similarity-based branching.

MAMMOth participated in AIUK 2023 hosted by the Alan Turing Institute, UK’s national showcase of data science and AI with 3.000+ participants. It took place on 21st and 22nd of March 2023. Event’s webpage:


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