Research database

CISC - Collaborative Intelligence for Safety Critical systems

48 months (2025)
Principal investigator(s):
Project type:
UE-funded research - H2020 - Excellent Science - Marie Curie
Funding body:
Project identification number:
PoliTo role:


The European Commission, in its guidelines on ethics in artificial intelligence published in April 2019, has recognised the importance of a 'human-centric' approach to AI that is respectful of European values. Dedicated training schemes to prepare for this required socio-economic change are now needed. AIs should be able to collaborate with (rather than replace) humans. Safety critical application of AI technology are ‘human-in-the-loop’ scenarios, where AI and humans work together, as manufacturing processes, IoT systems, and critical infrastructure (System Safety Engineering). The concept of Collaborative Intelligence is essential in these fields: The CISC EID will nurture and train 14 world class-leading Collaborative Intelligence Scientists and provide a blue-print for postgraduate training in Collaborative Intelligence for safety critical scenarios. The development of Collaborative Intelligence systems requires an interdisciplinary skillset 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 skillset necessary to carry-out the major tasks required to develop a Collaborative Intelligence system: (1) Modelling the dynamics of system behaviours for the productive 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. (4) Managing the Legal and Ethical implications in the use of physiology-recording wearable sensors and human performance data in AI algorithms.



  • Adient Interiors d.o.o.
  • European DIGITAL SME Alliance
  • Fakultet Inzenjerskih Nauka Univerziteta u Kragujevcu
  • Irish Manufacturing Research
  • IVECO Espana sl
  • mBrainTrain
  • PILZ Ireland Industrial Automation
  • Technological University of Dublin - Coordinator
  • Università degli Studi di Milano
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ERC sectors

PE6_12 - Scientific computing, simulation and modelling tools
PE6_9 - Human computer interaction and interface, visualisation and natural language processing
PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
PE6_7 - Artificial intelligence, intelligent systems, multi agent systems
LS1_1 - Molecular interactions
LS1_10 - Structural biology (NMR)

Sustainable Development Goals

Obiettivo 12. Garantire modelli sostenibili di produzione e di consumo


Total cost: € 3,605,667.12
Total contribution: € 3,605,667.12
PoliTo total cost: € 558,801.33
PoliTo contribution: € 558,801.33

Communication activities