CHIST-ERA is a consortium of research funding organisations in Europe and beyond supporting use- inspired basic research in Information and Communication Technologies (ICT) or at the interface between ICT and other domains. The CHIST-ERA consortium is itself supported by the European Union's Future & Emerging Technologies (FET) programme. CHIST-ERA promotes novel and multidisciplinary research with the potential to lead to significant technology breakthroughs in the long term. The funding organisations jointly support high risk and high impact research projects selected in the framework of CHIST-ERA, in order to reinforce European capabilities in promising emerging topics.
Research Targeted in the Call
The CHIST-ERA (www.chistera.eu) consortium has created a common funding instrument to support European research projects that engage in long-term research in the area of ICT and ICT-based sciences. Through this instrument, the national/regional funding organisations of CHIST-ERA support and join the Horizon 2020 Future and Emerging Technologies (FET) agenda. By launching joint European calls, they can support more diverse research communities, who are able to tackle the most challenging and novel research topics.
Each year, CHIST-ERA launches a call for research projects in two new topics of emergent scientific importance.
In previous years, CHIST-ERA calls have targeted quantum computing, consciousness, knowledge extraction, low-power computing, intelligent user interfaces, smart communication networks, adaptive machines, distributed computing, trustworthy cyber-physical systems, human language understanding, security and privacy in the IoT, terahertz communication, lifelong learning for intelligent systems, visual analytics, object recognition and manipulation by robots, big data and process modelling for smart industry, analog computing for artificial Intelligence and smart computing in networks.
This year's call concerns the following topics:
Explainable Machine Learning-based Artificial Intelligence (XAI);
Novel Computational Approaches for Environmental Sustainability (CES).
A workshop was held in Tallinn (Estonia) on 11-13 June 2019, bringing together researchers from across a range of research communities and countries, to identify the challenges and promising research directions within the two selected topics. This open consultation has formed the scope of this call.
CHIST-ERA projects should be of a FET-like nature and contribute to the development of the European research and innovation capacity in the technology domain of the call topics. The transformative research done in CHIST-ERA should explore collaborative advanced interdisciplinary science and/or cutting-edge engineering with the potential to initiate or foster new lines of technology and help Europe grasp leadership early on in promising future ICT and ICT-based areas with potential for significant impact in the long term.
Open access to publications and research data is a key asset to leverage on research funding. Applicants are encouraged to consider approaches promoting open access starting from the project preparation stage (see p. 8 about CHIST-ERA developing policy and ongoing activities).
To widen participation throughout Europe, CHIST-ERA projects are encouraged to include partners from the so-called Widening Countries participating in the call: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Portugal, Romania, Slovakia and Turkey.
To build leading innovation capacity across Europe and connect with industry, CHIST-ERA projects are encouraged to involve key actors that can make a difference in the future, for example excellent young researchers, ambitious high-tech SMEs etc.
1st Topic: Explainable Machine Learning-based Artificial Intelligence (XAI)
Explanation of decisions made by Artificial Intelligence (AI) systems is seen as important for the trust and social acceptance of AI. It is likely in the future that there will be a ‘right to an explanation' for decisions that affect an individual. The objective of research on this topic is to make machine learning- based AI explainable.
To do this effectively, it is expected that explanation will need to be designed and integrated into AI systems from the outset, including the data collection and training of algorithms that are the basis of machine learning-based AI.
Along with the technical challenges, it is important to consider that explanation is required at different levels for different stakeholders with different levels of technical knowledge, and in different application domains. It is also important to measure the effectiveness of the explanation at the human and the technical levels, for example by evaluating how transparency, trust and usability are enhanced.
Applicants should also consider the following:
Give due consideration to performance evaluation and experiment reproducibility
The benefits of international collaboration
Co-creation of projects with stakeholders, including end users, policy makers and industry
Potential for development of standards or frameworks
Responsible research and innovation including: Use and protection of data; The legal and ethical issues of providing explanations (what level of explanation is required or appropriate for whom); Open access to research data and publications
Development of novel, ambitious and reliable technologies for the different components of explainable machine learning-based AI, including: AI systems with integrated explanations in a variety of application areas; Frameworks for integrating explainability into AI (Explainability by Design); Methods for putting explainability into current AI systems; Use cases in specific application areas
Identification of new opportunities and applications fostered through explainable AI
Enhanced interdisciplinarity; Stakeholders involvement in design and implementation of explainable AI systems; Consideration of the ethical and social aspects of explainability in AI systems
Widened participation throughout Europe by involving partners from the Widening Countries
Reinforced innovation capacity across Europe by involvement of key actors, for example young researchers, high-tech SMEs or first-time participants
With the challenge of environmental changes being highlighted, it is important that scientists are able to understand and model the environment so they can understand and predict upcoming changes. As environmental models become more complex and more adaptable in real time, it is necessary to change the way we work with these models, to be more integrative, more reactive and reduce the amount of computational power being used. This will improve the computational models that we have and allow better predictions on the future of our planet.