Project description
Making AI explainable and traceable for critical autonomous systems
The artificial intelligence (AI) needed for complex autonomous tasks like self-driving cars depends on deep learning techniques. However, safety requirements mean that such techniques must also be explainable and traceable. The EU-funded SAFEXPLAIN project plans to solve this issue by creating new explainable deep learning solutions with end-to-end traceability that comply with functional safety requirements for critical autonomous AI-based systems while preserving high performance. Project work will include novel approaches to explain whether predictions can be trusted, and new strategies to prove correct operations. The project consists of a collaboration between three eminent European research centres and will conduct three case studies in the automotive, space and railway sectors.
Objective
Deep Learning (DL) techniques are key for most future advanced software functions in Critical Autonomous AI-based Systems (CAIS) in cars, trains and satellites. Hence, those CAIS industries depend on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost.
There is a fundamental gap between Functional Safety (FUSA) requirements of CAIS and the nature of DL solutions needed to satisfy those requirements. The lack of transparency (mainly explainability and traceability), and the data-dependent and stochastic nature of DL software clash against the need for deterministic, verifiable and pass/fail test-based software solutions for CAIS.
SAFEXPLAIN tackles this challenge by providing a novel and flexible approach to allow the certification – hence adoption – of DL-based solutions in CAIS by (1) architecting transparent DL solutions that allow explaining why they satisfy FUSA requirements, with end-to-end traceability, with specific approaches to explain whether predictions can be trusted, and with strategies to reach (and prove) correct operation, in accordance with certification standards. SAFEXPLAIN will also (2) devise alternative and increasingly complex FUSA design safety patterns for different DL usage levels (i.e. with varying safety requirements) that will allow using DL in any CAIS functionality, for varying levels of criticality and fault tolerance.
SAFEXPLAIN brings together a highly skilled and complementary consortium to successfully tackle this endeavor including 3 research centers, RISE (AI expertise), IKR (FUSA expertise), and BSC (platform expertise); and 3 CAIS case studies, automotive (NAV), space (AIKO), and railway (IKR). SAFEXPLAIN DL-based solutions are assessed in an industrial toolset (EXI). Finally, to prove that transparency levels are fully compliant with FUSA, solutions are reviewed by internal certification experts (EXI), and external ones subcontracted for an independent assessment.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.2.4 - Digital, Industry and Space
MAIN PROGRAMME
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HORIZON.2.4.5 - Artificial Intelligence and Robotics
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-RIA - HORIZON Research and Innovation Actions
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-CL4-2021-HUMAN-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08034 BARCELONA
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.