Explainable and Robust AI (AI Data and Robotics Partnership) (RIA)
Closed
Status as published by the data source.
Expected Outcome:
Projects are expected to contribute to one of the following outcomes:
• Enhanced robustness, performance and reliability of AI systems, including awareness of the limits of operational robustness of the system
• Improved explainability and accountability, transparency and autonomy of AI systems, including awareness of the working conditions of the system Scope:
Trustworthy AI solutions, need to be robust, safe and reliable when operating in real-world conditions, and need to be able to provide adequate, meaningful and complete explanations when relevant, or insights into causality, account for concerns about fairness, be robust when dealing with such issues in real world conditions, while aligned with rights and obligations around the use of AI systems in Europe. Advances across these areas can help create human-centric AI[1], which reflects the needs and values of European citizens and contribute to an effective governance of AI technologies.
To achieve robust and reliable AI, novel approaches are needed to develop methods and solutions that work under other than model-ideal circumstances, while also having an awareness when these conditions break down. To achieve trustworthiness, AI system should be sufficiently transparent and capable of explaining how the system has reached a conclusion in a way that it is meaningful to the user, while also indicating when the limits of operation have been reached.
The purpose is to advance AI-algorithms that can perform safely under a common variety of circumstances, reliably in real-world conditions and predict when these operational circumstances are no longer valid. The research should aim at advancing robustness and explainability for a generality of solutions, while leading to an acceptable loss in accuracy and efficiency, and with known verifiability and reproducibility. The focus is on extending the general applicability of explainability and robustness of AI-systems by foundational AI and machine learning research. To this end, the following methods may be considered but are not necessarily restricted to:
• data-efficient learning, transformers, reinforcement learning, federated and edge-learning, automated machine learning, or any combination thereof for improved robustness and explainability.
• hybrid approaches integrating learning, knowledge and reasoning, model-based approaches, neuromorphic computing, or other nature-inspired approaches and other forms of hybrid combinations which are generically applicable to robustness and explainability.
• continual learning, active learning, long-term learning and how they can help improve robustness and explainability.
• multi-modal learning, natural language processing, speech recognition and text understanding taking multicultural aspects into account for the purpose of increased operational robustness and the capability to explain alternative formulation[2]. Multidisciplinary research activities should address all of the following:
• Proposals should involve appropriate expertise in all the relevant disciplines, and where appropriate Social Sciences and Humanities (SSH), including gender and intersectional knowledge to address concerns around gender, racial or other biases. etc.
• Proposals are expected to dedicate tasks and resources to collaborate with and provide input to the open innovation challenge under HORIZON-CL4-2023-HUMAN-01-04 addressing explainability and robustness. Research teams involved in the proposals are expected to participate in the respective Innovation Challenges.
• Contribute to making AI and robotics solutions meet the requirements of Trustworthy AI, based on the respect of the ethical principles, the fundamental rights including critical aspects such as robustness, safety, reliability, in line with the European Approach to AI. Ethics principles needs to be adopted from early stages of development and design. All proposals are expected to embed mechanisms to assess and demonstrate progress (with qualitative and quantitative KPIs, benchmarking and progress monitoring), and share communicable results with the European R&D community, through the AI-on-demand platform or Digital Industrial Platform for Robotics, public community resources, to maximise re-use of results, either by developers, or for uptake, and optimise efficiency of funding; enhancing the European AI, Data and Robotics ecosystem through the sharing of results and best practice.
In order to achieve the expected outcomes, international cooperation is encouraged, in particular with Canada and India.
This topic is cancelled and replaced by topic HORIZON-CL4-2024-HUMAN-03-02 under Call HORIZON-CL4-2024-HUMAN-03.
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Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project – see General Annex B.
[1] A European approach to artificial intelligence | Shaping Europe’s digital future (europa.eu)
[2] Research should complement build upon and collaborate with projects funded under topic HORIZON-CL4-2023-HUMAN-01-03: Natural Language Understanding and Interaction in Advanced Language Technologies
- Status
- Closed
- Deadline
- (time not stated)
- Opens
- Published
- Country
- European Union (EU-wide)
- Programme
- Horizon Europe (HORIZON)
- Official page
- Open at the source portal
Other calls under Horizon Europe (HORIZON)
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- Advanced TSO control rooms to enhance grid observability, stability and resilience
- Advanced Distribution Management Systems (ADSM) for more efficient and flexible distribution grids
- Community of practice - Data-Driven Decision-Making in Energy
- Industrial processes and equipment for innovative, reliable and scalable tandem technologies (EUPI-PV Partnership)
- Integrated Approaches for Retrofitting Infrastructures with Innovative Energy Storage Technologies
- Demonstration of hydropower technologies for efficient and forward-looking refurbishment of existing hydropower plants
- Delivery of industrial CCUS clusters – Societal Readiness pilot
All calls under this programme
Similar opportunities
- EU Frontier AI Initiative: Developing frontier AI solutions that are safe and computationally efficient within Apply AI (RIA)
- New approaches for Human/AI collaboration for the workforce of the future (RIA) (Made in Europe and AI, Data and Robotics partnerships)
- International cooperation in AI (IA)
- New approaches for decentralized, federated and sustainable AI data processing (RIA)
- Apply AI: Challenge-Driven AI Innovation Booster in Apply AI prioritised sectors (RIA) (Partnership in AI, Data and Robotics)
- DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems
Where this came from
- Source document
- https://ec.europa.eu/info/funding-tenders/opportunities/data/topicDetails/horizon-cl4-2024-human-01-06.json
- Document fingerprint
409370ac2323cd8e(SHA-256, first 16 hex characters)- Retrieved
- First recorded here
What has changed
- url first recorded as https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl4-2024-human-01-06 on
- currency first recorded as EUR on
- amount_max first recorded as 0.00 on
- amount_min first recorded as 0.00 on
- budget_total first recorded as 0.00 on
- deadline first recorded as 2024-03-19 on
- opens_on first recorded as 2023-11-15 on
- published_on first recorded as 2022-12-07 on
- status_basis first recorded as source_status on
- status first recorded as closed on
- title first recorded as Explainable and Robust AI (AI Data and Robotics Partnership) (RIA) on
Data source
© European Union, 2026. Source: EU Funding & Tenders Portal. Reused under Commission Decision 2011/833/EU — CC BY 4.0.
Retrieved from the source on .
The source last updated this document on Tue, 17 Feb 2026 17:42:50 GMT.
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