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In the laboratory, there is also active a working group in Remote Sensing, with a focus on the research and development of applications and computational processes.
Novel decision support systems that encapsulate hypothesis testing methods as well as research on innovative decision support models that are based upon simulation of real business processes and market conditions.
Intuitive human machine interaction interfaces that fully employ interactive visualizations and enable cooperative schemes in real-time for improving decision making process.
Techniques for delivering distributed computational intelligence fully coupled with decentralized ledger architectures (block-chain) for delivering privacy-preserving and personalized data analysis among trusted ecosystems.
Self-training algorithms fully exploiting historical data with robust capabilities for detecting patterns in real-time, including technologies for predictive analytics.
Deployment of machine learning and artificial intelligence methods for the processing and mining of important information-related features as well as to identify patterns through deep neural networks, multimodal optimization, clustering, etc.
Z-Inspection® is a holistic process used to evaluate the trustworthyness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI.
The Z-Inspection® process is distributed under the terms and conditions of the Creative Commons (Attribution-NonCommercial-
and it is published in IEEE Transactions on Technology and Society.
Z-Inspection® is listed in the new OECD Catalogue of AI Tools & Metrics https://oecd.ai/en/