Trustworthy AI for Critical Systems
The aim of this challenge is to build and assess Trustworthy AI and lower in this way the obstacles for Walloon companies willing to deploy AI solutions in regulated sectors.
What's at stake
AI systems have become increasingly complex, evolving from models with hand-crafted rules (human supervision/intervention) to models that create other models. Many appear as black boxes, which creates mistrust and prevents end-users from relying on otherwise highly efficient technologies. This evolution has pushed the European Union and regulators from many industries to look for ways to certify AI-based critical systems (e.g., airplanes or cars) before manufacturing and using them. The need to certify AI technologies prior to their market deployment is a significant barrier to the adoption of AI, especially in sensitive sectors such as aeronautics, space or medicine. Sectors where they could be precisely of precious support to human decision-making.
Establishing a whole framework for the trustworthy development of artificial intelligence. Europe has turned this challenge into one of its priorities in order to differentiate itself from other major competitive markets. Yet, this framework must offer clear and applicable guidelines so that the initial objective of encouraging companies to use these technologies is indeed served.
AI possible solutions
Guided by the four ethical principles and seven requirements for Trusted AI put forward by the European Commission, various technological avenues will be explored such -as explainability – interpretability; robustness - adversarial attack; ethic - bias – fairness; and safety - uncertainty quantification. Generic tools will be created guiding the creation of algorithms and facilitating in this way the deployment of trusted AI systems. To the extent possible these tools will anticipate the regulations existing in different sectors such as the protection of patients’ data in healthcare or drone regulations in aeronautics.
Key AI tech topics :
- Explainable AI (XAI)
- Fair / non-biased AI systems
- Robustness in AI systems
Get in touch
Emmanuel Jean (firstname.lastname@example.org)