-- Explainable AI (XAI) has recently emerged proposing a set of techniques attempting to explain machine learning (ML) models. The recipients (explainee) are intended to be humans or other intelligent virtual entities. Transparency, trust, and debuging are the underlying features calling for XAI. However, in real-world settings, systems are distributed, data are heterogeneous, the “system” knowledge is bounded, and privacy concerns are subject to variable constraints. Current XAI approaches cannot cope with such requirements.
HES-SO
University of Applied Sciences and Arts Western Switzerland
Find out more! UNIBO
Alma Mater Studiorum Università di Bologna
Find out more! UNILU
University of Luxembourg
Find out more! OZU
Özyeğin University
Find out more! LIST
Luxembourg Institute of Science and Technology
The team has multidisciplinary competences sharing the Multi-Agent Systems as common thread.
HES-SO People (from Switzerland) Prof. Michael I. Schumacher
Full Professor at HES-SO
Personal Homepage Dr. Davide Calvaresi
Senior researcher at HES-SO
Personal Homepage Dr. Jean-Paul Calbimonte
Senior researcher at HES-SO
Personal Homepage Victor Hugo Contreras Ordonez
Software Tools PSyKE: A Python library for the extraction of symbolic knowledge from ML predictors. PSyKI: A Python library for the injection of symbolic knowledge into ML predictors. DEXiRE: A Python library for rule extraction from Deep Learning models. Pro-DEXiRE: A Python library that complements DEXiRE’s rule based explanations with probabilistic reasoning. Datasets Recipes dataset: Dataset collected querying GPT API with 7000 recipes.
Deliverables [D1.4] Data Management Plan (DMP) [D2.1] Tech report on symbolic knowledge extraction and injection [D2.2] Scientific paper on symbolic knowledge extraction and injection [D2.3] Software libraries supporting extraction and injection [D3.1] Technical report detailing the developed models and data integration [D3.2a] Scientific paper focusing on heterogeneous data integration [D3.2b] Scientific papers focusing on conflict resolution [D4.1] Technical report detailing the developed user model and agent-based profiling [D5.
