Τhe following methods may be considered but are not necessarily restricted to:

• data-efficient learning, transformers and alternative architectures, self-supervised learning, fine-tuning of foundation models, 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, neurosymbolic methods, 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.

Multidisciplinary research activities should address all of the following:

• Proposals should involve appropriate expertise in all the relevant sector specific use cases and 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 and possible sector-specific forums 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.