Martina Bacaro

PhD curriculum on Trustworthy AI

The TAILOR Network of Excellence is proud to present a PhD curriculum on Trustworthy AI. The TAILOR curriculum gives a detailed specification of the structure and content of a possible training programme that could be delivered by academic institutions as part of a PhD degree. Coordinated by researchers Peter Flach and Miquel Perello Nieto of

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Invited Keynote about TAILOR network and Trustworthy AI at “Innovate&Talk” by NTT Data 

On 31 January 2023, Dr. Silke Balzert-Walter (German Research Centre for Artificial Intelligence, DFKI – coordinator of TAILOR WP8 (Industry, Innovation and Transfer Program) was invited by NTT Data to present TAILOR and WP8-activities in a keynote titled “Trustworthy AI in the European AI Networks of Excellence”.   NTT Data is a global industrial company outside the TAILOR project specialised

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TAILOR scientists at AAMAS and CVPR

We are thrilled to announce that some TAILOR scientists will be participating in two of the most prestigious conferences in the AI field: AAMAS and CVPR. At AAMAS 2023 (the 22nd International Conference on Autonomous Agents and Multi-Agent Systems) in London, UK, from May 29th to June 2nd, TAILOR scientists will be presenting groundbreaking research

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Ana Paiva elected in the AAAI 2023 Fellow Class

The Association for the Advancement of Artificial Intelligence (AAAI) established the Fellows program in 1990 to acknowledge individuals who have made significant and prolonged contributions to the field of AI, typically spanning at least ten years. Each February, AAAI members nominate individuals they believe have achieved extraordinary distinction in AI. Then, a committee of nine

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EGOV-CeDEM-EPART 2023 conference at Corvinus University of Budapest, 5-7 September 2023

The annual conference of the International Federation for Information Processing 8.5 (ICT & Public Administration) Working Group on e-Government, e-Democracy and e-Participation this year will be held at Corvinus University of Budapest (https://uni-corvinus.hu/?lang=en) 5-7 September 2023.  Details on the EGOV-CeDEM-EPART 2023 conference may be found at https://dgsociety.org/egov-2023/. The Deadline for paper submission is 31 March and

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New projects funded by Collaboration Exchange Fund

The Collaboration Exchange Fund (CEF) awarded the first projects submitted by PhD students within the TAILOR network. The TAILOR Collaboration Exchange Fund aims to enhance collaboration between TAILOR partners by funding the mobility of PhD students. While Connectivity Fund (https://tailor-network.eu/connectivity-fund/) is dedicated to funding exchanges between TAILOR and non-TAILOR scientists, the CEF is for boosting

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Graph Representation Learning for Solving Combinatorial Optimization Problems

Ya Song PhD student at Eindhoven University of Technology Abstract: In the research field of solving combinatorial optimization problems, many studies have considered combining machine learning with optimization algorithms and proposed so-called learning-based optimization algorithms. Compared to traditional handcrafted algorithms, these methods can automatically extract relevant knowledge from training data and require less domain knowledge. In

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Feedback from TAILOR scientists at the 37th AAAI Conference on Artificial Intelligence

The AAAI Conference on Artificial Intelligence promotes theoretical and applied AI research as well as intellectual interchange among researchers and practitioners. The technical program features substantial, original research and practices. Conference panel discussions and invited presentations identify significant social, philosophical, and economic issues influencing AI’s development throughout the world. The 37th AAAI Conference on Artificial

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Causal Analysis for Fairness of AI Models

Martina cinquini PhD student at the University of Pisa Abstract: Artificial Intelligence (AI) has become ubiquitous in many sensitive domains where individuals and society can potentially be harmed by its outputs. In an attempt to reduce the ethical or legal implications of AI-based decisions, the scientific community’s interest in fairness-aware Machine Learning has been increasingly

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