Collaborative intelligence – combining the best of machine and human (AI Data and Robotics Partnership) (RIA)

Topic description

ExpectedOutcome:

Projects are expected to contribute to the following outcomes:

• Demonstrate the value of human-machine collaboration and interaction by improved effectiveness, intuitiveness, efficiency, completeness, limits of knowledge indication and other objective or quantifiable subjective measures.
• Demonstrate how collaborative decision-making improves over human decision-making and that the collaborative decisions cover all stages of reasoning (that they are based on an improved coverage of data and knowledge sources, on an improved analytic ability to reason from input to output, and on a well-communicated decision).

Proposals are expected to address at least one of the expected outcomes.

The R&I priorities require work at different levels, including both foundational research and well-studied piloting efforts, concentrated in impactful projects, bringing critical mass of expertise and investment to demonstrate potential for more than one major application sectors respectively.

Research should focus on:

• foundational research towards the next generation of collaborative AI, bringing excellence, critical mass and novel approaches as well as quantitatively proven improvement in the levels of human-machine collaboration.
• simulations and experimentation (with and without humans in the loop) to explore the consequences of different interventions and/or to explore the design approaches that help manage decision making.
• integrating advances from [effective, efficient, anticipative, multi-modal] human-computer interaction and from [incremental, continually learned, or anticipative], automatic reasoning systems in order to create new generations of collaborative AI-systems that better and more naturally serve human needs. The means of collaboration can cover the whole range of multi-modal stimuli: lingual, image, video, sound and other forms of interaction, whatever is arguably the most appropriate in the interaction process
• Advancing human-machine collaboration and interaction - operational for a broad range of AI-reasoning systems and applicable to a broad range of application areas of AI.

At least one proposal will be selected with a focus on human-machine collaboration and interaction and at least one with a focus on collaborative decision-making. Proposals should clearly mention which of the two areas they address.

Multidisciplinary research activities should address all of the following:

• Proposals should involve appropriate expertise in Social Sciences and Humanities (SSH), including knowledge on gender and intersectional inequalities.
• Research should build on existing standards or contribute to standardisation. Interoperability for data sharing should be addressed, notably through the implementation of the FAIR data principles and adopting standardised and discipline-oriented metadata schemas and ontologies.
• 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.
• Projects should also build on or seek collaboration with existing projects and develop synergies with other relevant European, national or regional initiatives, funding programmes and platforms.
• 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, and gender-sensitivity should be considered, where relevant.

All proposals are expected to embed mechanisms to assess and demonstrate progress (with qualitative and quantitative KPIs, benchmarking and progress monitoring, as well as illustrative application use-cases demonstrating concrete potential added value), 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.

Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project – see General Annex B.