Trustworthy, Ethical and Beneficial-to-All Multiagent Systems Solutions for Social Ridesharing and the Hospitality Industry

Georgios Chalkiadakis Professor at Technical University of Crete Current mobility-as-a-service platforms have departed from the original objectives of the sharing economy-inspired social ridesharing paradigm: regrettably, they view drivers as taxi workers; focus on profit maximization rather than fair travel costs’ allocation; and disregard essential private preferences of users (relating for instance to their feeling of […]

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Meta-learning for scalable multi-objective Bayesian optimization

Jiarong Pan PhD at Bosch Center for Artificial Intelligence Abstract: Many real-world applications consider multiple objectives, potentially competing ones. For instance, for a model deciding whether to grant or deny loans, ensuring accurate while fair decisions is critical. Multi-objective Bayesian optimization (MOBO) is a sample-efficient technique for optimizing an expensive black-box function across multiple objectives.

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Using robustness distributions to better understand fairness in Neural Net-works

Annelot Bosman PhD at Universiteit Leiden This project aims to investigate fairness from a new perspect- ive, namely by using robustness distributions, introduced in previous work. Investig- ating robustness in neural networks is very computationally expensive and as such the community has directed focus on increasing verification speed. Robustness distributions, although expensive to obtain, have

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TAILOR Selected Papers: April

Every month, we want to acknowledge some valuable TAILOR papers, selected among the papers published by scientists belonging to our network by TAILOR principal investigator Fredrik Heintz.The list of the most valuable papers gathers contributions from different TAILOR partners, each providing valuable insights on different topics related to TrustworthyAI.Stay tuned for other valuable insights and

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Translating between AI Evaluation and Job Tasks in the human workplace for trustworthy and reliable AI deployment

Marko Tesic Post-doc at LCFI, University of Cambridge, UK Recent advancements in AI, particularly in language modeling, have rekindled concerns about the potential automation of certain roles within the human workforce. To better understand which roles are susceptible to automation and to ensure the trustworthy and reliable deployment of AI, I aim to establish a

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Evaluation of cognitive capabilities for LLMs

Lorenzo Pacchiardi Post-doc at University of Cambridge Artificial Intelligence (AI) systems (such as reinforcement-learning agents and Large Language Models, or LLMs) are typically evaluated by testing them on a benchmark and reporting an aggregated score. As benchmarks are constituted of instances demanding various capability levels to be completed, the aggregated score is uninformative of the

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