BIAS workshop at ECML PKDD 2026

22 May 2026

Prof. Eirini Ntoutsi will co-organize the 6th Workshop on Bias and Fairness in AI in conjunction with ECML PKDD 2026.

The workshop will take place on September 7, 2026, in Naples, Italy.

Bias and fairness in AI systems are central challenges in modern AI and are becoming increasingly important with the rise of Generative AI, agentic systems, and the widespread deployment of AI-powered applications.

The workshop brings together researchers and practitioners working on fairness, bias, accountability, and responsible AI, with a focus on developing reliable and trustworthy machine learning systems and addressing the evolving technical and societal dimensions of bias in AI.

ECML PKDD is one of the leading European conferences on machine learning and data mining, attracting an international research community from academia and industry.

Aktuelles

Talk at LMU Statistics Department

Prof. Eirini Ntoutsi gave a talk at the Departmental Seminar of the Statistics Department at LMU on Socio-Economic Parity in AI Systems.

Paper accepted in AI & Society journal

Our paper “Not a statistical accident: framing bias as a semiotic property of image datasets” has been accepted for publication in AI & Society. Congratulations to Simone and all co-authors!

MMM-Fair presented in the Demo Track at CIKM 2025

“MMM-Fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Tradeoffs” was presented in the Demo Track at CIKM 2025, held in Seoul, Republic of Korea.

Kick-off Workshop of the DZdA Project – German Center for Digital Tasks in Higher Education Teaching

The kick-off workshop of the project German Center for Digital Tasks in Higher Education Teaching (DZdA) took place as a two-day intensive event with all nine project partners from universities across Germany. The host was OTH Amberg-Weiden, which, together with the strategic consulting firm pictomind GmbH, facilitated the event. The goal of the workshop was to establish a shared understanding of the project, define strategic objectives, and lay the foundation for successful collaboration.

Fair & Bias-Aware AI: MAMMOth Concludes

The Horizon Europe-funded project MAMMOth (Multi-Attribute, Multimodal Bias Mitigation in AI Systems) officially concluded on 31 October 2025 after three years of pioneering work to make Artificial Intelligence fairer, more inclusive, and more accountable. The project brought together academic, industrial, and societal partners to address bias in AI systems and to deliver practical tools and knowledge for fairness-aware AI development. Its outcomes provide policymakers, technology developers, and society with concrete strategies for embedding fairness at the centre of AI innovation.

Paper accepted: IEEE Big Data 2025

Our Paper "A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective" was accepted at IEEE Big Data 2025. Congratz, Siamak!