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Collaborative Intelligence for Safety Critical systems (CISC)

Progetto
The European Commission "The European Commission’s guidelines on ethics in artificial intelligence" (AI), published in April 2019, recognised the importance of a 'human-centric' approach to AI that is respectful of European values. Dedicated training schemes to prepare for the integration of ‘human-centric’ AI into European innovation and industry are now needed. AIs should be able to collaborate with (rather than replace) humans. Safety critical applications of AI technology are ‘human-in-the-loop’ scenarios, where AI and humans work together, as manufacturing processes, IoT systems, and critical infrastructures. The concept of Collaborative Intelligence is essential in these scenarios. The CISC EID will nurture and train 14 world class-leading Collaborative Intelligence Scientists for safety critical situations and provide a blue-print for postgraduate training in this area. The development of Collaborative Intelligence systems requires an interdisciplinary skill set blending expertise across AI, Human Factors, Neuroergonomics and System Safety Engineering. This inter-disciplinary skill-set is not catered for in traditional training courses at any level. The CISC training programme will develop Collaborative Intelligence Scientists with the expertise and skill set necessary to carry-out the major tasks required to develop a Collaborative Intelligence system: (1) Modelling the dynamics of system behaviours for the production processes, IoT systems, and critical infrastructures (System Safety Engineering); (2) Designing and implementing processes capable of monitoring interactions between automated systems and the humans destined to use them (Human Factors/Neuroergonomics); (3) Using data analytics and AI to create novel human-in-the-loop automation paradigms to support decision making and/or anticipate critical scenarios; and, (4) Managing the Legal and Ethical implications in the use of physiologyrecording wearable sensors and human performance data in AI algorithms.
  • Dati Generali
  • Aree Di Ricerca
  • Pubblicazioni

Dati Generali

Partecipanti

DAMIANI ERNESTO   Responsabile scientifico  

Dipartimenti coinvolti

Dipartimento di Informatica Giovanni Degli Antoni   Principale  

Tipo

H20MCITNIF - Horizon 2020_Marie Skłodowska-Curie actions-Innovative Training Network (ITN)/Individual Fellowships (IF)

Finanziatore

EUROPEAN COMMISSION
Organizzazione Esterna Ente Finanziatore

Periodo di attività

Gennaio 1, 2021 - Giugno 30, 2025

Durata progetto

54 mesi

Aree Di Ricerca

Settori


Settore INF/01 - Informatica

Pubblicazioni

Pubblicazioni (4)

A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications 
FRONTIERS IN ROBOTICS AND AI
FRONTIERS MEDIA S.A.
2024
Articolo
Open Access
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Collaborative Intelligence for Safety-Critical Industries: A Literature Review 
INFORMATION
MDPI
2024
Articolo
Open Access
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User-Centered Evaluation Framework for Telerobot Interface and Interaction factors -- A Case Study on Medical Device Manufacturing 
RESEARCH PUBLISHING
2023
Contributo in Atti di convegno
Open Access
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Neuro-Symbolic AI for Sensor-based Human Performance Prediction: System Architectures and Applications 
RESEARCH PUBLISHING, SINGAPORE
2022
Contributo in Atti di convegno
Open Access
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