Mammoth https://validator.w3.org/feed/docs/rss2.html Deliverables D5.4 Final DCE and Policy Report D4.2 Stakeholder Evaluation of System, Processes and Results D4.1 Evaluation and Mitigation of Bias in the Use Cases The AI Creator’s AI Fairness Definition Guide Publications AI bias: overview, measurement, mitigation and application to computer vision Fairness-Aware ML Tutorial Series #6 Behind the Algorithm Home #5 Rethinking AI Final Press Release D5.3 MAMMOth’s Handbook: Best practices D3.3 Methods and Algorithms for Multidimensional Fairness in Multi-attribute Networks D3.2 Methods and Algorithms for Multidimensional Fairness-aware Classification D1.2 MAMMOth Βias Τoolkit Partners D2.3 Explainable Methods for Discovering Bias in Multimodal Data Project recommendations MAMMOth Technical Stakeholders Brief Fairness-Aware ML Tutorial Series Measuring Algorithmic Fairness Visual Bias Mitigator (VB-Mitigator) Training Protected attribute inventory for additional domains Tutorials 1st MAMMOth webinar 2nd MAMMOth webinar 3rd Webinar on “Critical AI literacy: Implications for education & public engagement with science” 4th Webinar on “Achieving Socio-Economic Parity through the Lens of EU AI Act” 5th Webinar on “Tackling feature bias in counterfactual explanations for mixed tabular data” #1 Can we trust AI? #2 Βeyond AI Bias #3 Mind over Machine #4 Κnowing & Not Knowing Algorithmen lernen zu diskriminieren February Newsletter Ethics guidelines for trustworthy AI 3rd Workshop on Bias and Fairness in AI May Newsletter Open Day Ethics: What do we want AI (not) to do? Workshop: Critical use of ChatGPT BIAS 2023 European Researchers’ Night 2023 Financial Case 2023 European Researchers’ Night AI Assistants Get More Credit Than Humans On the way to an unbiased AI-powered face verification AI Fairness Cluster Inaugural Conference