Deploying artificial intelligence (AI) successfully in Europe requires an integrated landscape for its adoption, and its development based on Europe’s unique characteristics, write Edward Curry of NUI Galway and Barry O’Sullivan of University College Cork. 

The two leading researchers from the Insight SFI Research Centre for Data Analytics are signatories to the largest public-private partnership agenda in AI, data, and robotics in the world.

Drive excellent research in Europe in AI and allied fields

Funded by industry and the European Commission, the Joint Strategic Research Innovation and Deployment Agenda for the Artificial Intelligence, Data and Robotics Partnership (SRIDA) will work to build an effective innovation ecosystem and drive excellent research in Europe in AI and allied fields.

Professor Barry O’Sullivan and Dr Edward Curry from the Insight SFI Centre for Data Analytics in Cork and Galway have helped to lead the development of the agenda, which will map a partnership that will support European values, to improve current business and to create new business in AI and data science. 

The partnership will drive innovation, uptake and acceptance of digital innovation across the bloc. It will support research, development and deployment, foster novel applications and stimulate public and private investment. It will be guided by ‘a humancentric and trustworthy European vision of AI’, according to members.

Insight’s Barry O’Sullivan said: "The Insight SFI Centre plays a leadership role at a European level in a variety of ways and this is reflected in the production of this SRIDA. As then president of the European AI Association I worked alongside Dr Edward Curry, vice-president of the Big Value Data Association, and others, on the delivery of the joint SRIDA.

"The five partner organisations consulted broadly with industry, associations, research groups and scientists across Europe through surveys, workshops and requests for written contributions, attracting hundreds of inputs. This is a major new policy direction for European AI, data and robotics innovation at a critical time for the sector."

Dr Edward Curry explained the importance of the initiative for the sector: "This strategic agenda sets out a clear roadmap to bring together three critical enabling technologies AI, data and robotics for the digital transformation of Europe. The partnership can drive this transformation by bringing together academic, industry, and startups within a pan-European innovation ecosystem with both public and private investments."

The Big Data Value Association (BDVA) and the European Robotics Association (euRobotics) have developed a joint Strategic Research and Innovation Agenda (SRIDA) for an AI, data and robotics partnership in Europe (S Zillner et al., 2019). 

In September 2020 BDVA, CLAIRE, ELLIS, EurAI and euRobotics announced the official release of the SRIDA for the AI, data and robotics partnership which unifies the strategic focus of each of the three disciplines.

A joint statement from the participating groups describes the objective of SRIDA: "The vision of the partnership is to boost European industrial competitiveness, societal wellbeing and environmental aspects to lead the world in developing and deploying value-driven trustworthy AI, data and robotics based on fundamental European rights, principles and values."

To deliver on the vision of the AI, data and robotic partnership, it is important to engage with a broad range of stakeholders. Each collaborative stakeholder brings a vital element to the functioning of the partnership and injects critical capability into the ecosystem created around AI, data and robotics by the partnership.

The mobilisation of the European AI, data and robotics ecosystem is one of the core goals of the partnership. It needs to form part of a wider ecosystem of collaborations that cover all aspects of the technology application landscape in Europe.

Many of these collaborations will rely on AI, data and robotics as a critical enabler to their endeavours. Both horizontal (technology) and vertical (application) collaborations will intersect within an AI, data and robotics ecosystem.

Figure 1: European AI, Data and Robotics Framework and Enablers (Sonja Zillner et al., 2020)

Figure 1 sets out the context for the operation of the AI, data and robotics. It clusters the primary areas of importance for AI, data and robotics research, innovation and deployment into three overarching areas of interest.

European AI, Data and Robotics Framework represents the legal and societal fabric that underpins the impact of AI on stakeholders and users of the products and services that businesses will provide.

The AI, data and robotics Innovation Ecosystem Enablers represent essential ingredients for effective innovation and deployment to take place. Finally, the Cross Sectorial AI, Data and Robotics Technology Enablers represent the core technical competencies that are essential for the development of AI, data and robotics systems.

The remainder of this section offers a summary of the European AI, Data and Robotics Framework, which is the core of the SRIDA (Sonja Zillner et al., 2020) developed by BDVA, euRobotics, ELLIS, EurAI and Claire. 

1. European AI, Data and Robotics Framework  

AI, data and robotics works within a broad framework that sets out boundaries and limitations on its use. In specific sectors, such as healthcare, AI, data and robotics operates within ethical, legal and societal contexts and within regulatory regimes that can vary across Europe.

Products and services based on AI, data and robotics are shaped by certification processes and standards and impact on users to deliver value compatible with European rights principles and values.

Critical to deploying AI, data and robotics is its acceptance by users and citizens, and this acceptance can only come when they can assign trust. This section explores this European AI, Data and Robotics Framework (Sonja Zillner et al., 2020) within which research, design, development and deployment must work. 

European fundamental rights, principles, and values 

On the one hand, the recent advances in AI, data and robotics technology and applications have fundamentally challenged ethical values, human rights and safety in the EU and globally.

On the other hand, AI, data and robotics offer enormous possibilities to raise productivity, address societal and environmental challenges and enhance the quality of life for everybody.

The public acceptance of AI, data and robotics is a prerequisite on it being trustworthy, ethical and secure and, without public acceptance, the full benefit of AI, data and robotics cannot be realised.

The European Commission has already taken action and formulated in its recent communications a vision for an ethical, secure and cutting-edge 'AI made in Europe' designed to ensure AI, data and robotics operates within an appropriate ethical and legal framework that embeds European values. The partnership (Sonja Zillner et al., 2020)will:

• Facilitate a multi-stakeholder dialogue and consensus-building around the core issue of trustworthiness by guiding and shaping a common AI, data and robotics agenda, and fostering research and innovation on trustworthy technologies;
• Seek to promote a common understanding among stakeholders of European AI, data and robotics fundamental, rights and values, so that each sector and community are informed and aware of the potential of AI, data and robotics as well as the risks and limitations of current technology and will develop guidance in the responsible implementation of AI, data and robotics;
• Establish the basis for identifying and expressing a European strategic viewpoint on rights, principles and values by providing clear links to relevant regulation, certification, and standardisation.

Capturing value for business, society, and people

Technical advances in AI, data and robotics are now enabling real world applications. These are leading to improved or new value-added chains being developed and integrated.

To capture these new forms of value, AI-based solutions may require innovative business models that redefine the way stakeholders share investments, risk, know-how and data and, consequently, value.

This alteration of value flow in existing markets is disruptive and requires stakeholders to alter their business models and revenue streams. These adjustments require new skills, infrastructure and knowledge, and organisations may have to buy in expertise or share data and domain know-how to succeed. This may be incredibly difficult if their underlying digitalisation skills, a prerequisite for AI, data and robotics adoption, are weak.

Even incremental improvements or more considerable changes carry risks and may create a reluctance to adopt AI, data and robotics. There may be little or no support for change within an organisation or value chain, especially when coupled to a lack of expertise.

Successful adoption of AI, data and robotics solutions requires a dialogue between the different stakeholders to design a well-balanced and sustainable value network incorporating all stakeholder's interests, roles and assets.

To support the adoption of AI, data and robotics applications, the partnership (Sonja Zillner et al., 2020) will stimulate discussions to align supply and demand perspectives of the diverse AI, data and robotics value networks partners. With the main focus on application areas and sectors that:

1. are crucial for the European economy
2. relate to critical infrastructure
3. have a social or environmental impact
4. can increase European competitiveness in AI

Policy, regulation, certification, and standards (PRCS) 

The adoption of AI, data and robotics depends on a legal framework of approval built on regulation, partly driven by policy, and an array of certification processes and standards-driven by industry. As AI, data and robotics is deployed successfully in new market areas, regulation and certification can lag behind, thereby creating barriers to adoption.

Similarly, a lack of standards and associated certification and validation methods can hold back deployment and the creation of supply chains and therefore, slow market uptake.

In some areas of AI, data and robotics, the market will move ahead and wait for regulation to react, but in many application areas existing regulation can present a barrier to adoption and deployment.

Most notably in applications where there is a close interaction with people, either digitally or physically, or where AI, data and robotics is operating in safety or privacy critical environments.

PRCS issues are likely to become a primary area of activity for the AI, data and robotics partnership. Increasingly it is regulation that is the primary lever for the adoption of AI/data/robotics-systems, particularly when physical interactions are involved or where privacy is a concern.

Similarly, the development of standards, particularly around data exchange and interoperability, will be key to the creation of a European AI, data and robotics marketplace.

Establishing ways that ensure conformity assessments of AI, data and robotics will underpin the development of trust that is essential for acceptance and therefore, adoption.

In addition, the partnership also has a role to advise on regulation that creates or has the potential to create, unnecessary barriers to innovation in AI, data and robotics. The partnership  (Sonja Zillner et al., 2020) will need to carry out the following activities to progress PRCS issues: 

• Identify key stakeholders in each area of PRCS and ensure there is good connectivity between them and to the AI, data and robotics ecosystem;
• Work with stakeholders and the emerging AI, data and robotics ecosystem infrastructure (digital innovation hubs, pilots, and data spaces) to identify key issues that impact on adoption and deployment in each major sector;
• Promote best practice in deployment regarding PRCS issues and provide signposts to demonstrators and processes that can accelerate uptake;
• Support and collaborate in standardisation initiatives, and the harmonisation of regulation across Europe to create a level AI, data and robotics single marketplace and connect with European and global standards and regulatory bodies;
• Foster the responsible testing of AI, data and robotics innovation in regulatory sandbox environments;
• Consolidate recommendations towards policy changes and provide support related impact assessment processes;
• Drive European thinking and needs towards international standardisation bodies. 

Innovation ecosystem enablers 

The innovation ecosystem enablers are essential ingredients for success in the innovation system. They represent resources that underlie all innovation activity across the sectors and along the innovation chain from research to deployment.

Each represents a key area of interest and activity for the partnership (Sonja Zillner et al., 2020) and each presents unique challenges to the rapid development of European AI, data and robotics.

Skills and knowledge

As traditional industry sectors undergo an AI, data and robotics transformation, so too must their workforces. There is a clear skills gap when it comes to AI, data and robotics.

However, while there are shortages of people with specific technical skills or domain knowledge; there is also the need to train interdisciplinary experts.

AI, data and robotics experts need insight into the ethical consequences posed by AI, by machine autonomy and big data automated processes and services, they need a good understanding of the legal and regulatory landscape, for example, GDPR, and the need to develop and embed trustworthiness, dependability, safety and privacy through the development of appropriate technology.

The partnership will work through its network to ensure that all stakeholders along the value chain, including citizens and users, have the understanding and skills to work with AI-enabled systems, in the workplace, in the home and online.

The partnership has a critical role to play in bringing together the key stakeholders; academia, industry, professional trainers, formal and informal education networks and policymakers.

These collaborations will need to examine regional strengths and needs in terms of skills across the skill spectrum, both technical and non-technical. It is critical to ensure that the skill pipeline is maintained to ensure the AI, data and robotics transformation of Europe is not held back. Some concrete actions the partnership (Sonja Zillner et al., 2020) will focus on:

• Promote equality and diversity within the current and future workforce and ensure diversity and balance in the educational opportunities that drive the skill pipeline;
• Ensure the alignment of curricula and training programmes for AI, data and robotics professionals with industry needs;
• Establish AI, data and robotics skills, both technical and non-technical, through certification mechanisms for university courses, professional and vocational training, and informal learning;
• Development of complementary short-courses related to AI aimed at decision-makers in industry and public administration, and those wishing to upgrade, enhance or acquire AI-based skills;
• Support for secondary education and adult learning to cover STEM skills including ethics, social, and the business aspects of AI together with the changing nature of work as well as support for vocational training.

Data for AI

In order to further develop AI, data and robotics technologies and meet expectations, large volumes of cross-sectoral, unbiased, high-quality and trustworthy data need to be made available. Data spaces, platforms and marketplaces are enablers, the key to unleashing the potential of such data.

There are however important business, organisational and legal constraints that can block this scenario such as the lack of motivation to share data due to ownership concerns; loss of control, lack of trust; the lack of foresight in not understanding the value of data or its sharing potential; the lack of data valuation standards in marketplaces; the legal blocks to the free flow of data and the uncertainty around data policies.

Additionally, significant technical challenges such as interoperability, data verification and provenance support, quality and accuracy, decentralised data sharing and processing architectures, and maturity and uptake of privacy-preserving technologies for big data have a direct impact on the data made available for sharing. The partnership (Sonja Zillner et al., 2020) will:

1. Create the conditions for the development of trusted European data sharing frameworks to enable new data value chain opportunities, building upon existing initiatives and investments (data platforms, i-spaces, big data innovation hubs). Data value chains handling a mix of personal, non-personal, proprietary, closed and open research data need to be supported. The partnership would promote open datasets and new open benchmarks for AI algorithms, subject to quality validation from both software engineering and functional viewpoints. 
2. Define specific measures to incorporate data sharing at the core of the data lifecycle for greater access to data, encouraging collaboration between data value chain actors in both directions along the chain and across different sectors. Additionally, the partnership will provide supportive measures for European businesses to safely embrace new technologies, practices and policies.
3. Facilitate co-ordination and harmonisation of member states' efforts and realise the potential of European-wide AI-digital services in the face of global competition. It would guide and influence standards concerning tools for data sharing, privacy preservation, quality verification, collaboration and interaction. Promote standardisation at European level but maintain collaboration with international initiatives for made-in-Europe AI to be adopted worldwide. 

Experimentation and deployment: are central levers for AI/data/robotics-based innovation because of the need to deploy in complex physical and digital environments. This includes safe environments for experimentation to explore the data value as well as to test the operation of autonomous actors.

AI, data and robotics driven innovations rely on the interplay of different assets, such as data, robotics, algorithms and infrastructure. For that reason, co-operation with other partners is central to gaining access to complementary assets.

This includes access to the AI, data and robotics ecosystem covering AI platform providers, data scientists, data owners, providers, consumers, specialised consultancy, etc. The (Sonja Zillner et al., 2020) will: 

• Stimulate co-operation between all stakeholders in the AI, data and robotics value chain around experimentation and deployment;
• Enable access to infrastructure and tools in combination with data sets covering the whole value chain as a basis for doing experiments to support development and deployment;
• Support the creation and linking of digital innovation hubs, centres of excellence and all other EC initiatives;
• Support AI, data and robotics based incubators as well as testbed developments as well as promote initiatives that enable SME access to infrastructure and tools at low cost;
• Foster set-ups that bring together industrial user with research excellence, domain experts with data scientists, aiming to fill the gaps between domain/business and technical expertise.

Cross-sectorial AI, data and robotics technology enablers 

The last part of the framework is the technology enablers for building successful AI products and services. Each embodies the concept that AI, data and robotics need to work in unison to achieve optimal function and performance. They represent the fundamental building blocks needed to create AI, data and robotics systems of all types.

The sensing and perception and knowledge and learning technology enablers create the data and knowledge on which decisions are made. These are used by the reasoning and decision-making technologies to deliver; edge and cloud-based decision making, planning, search and optimisation in systems, and the multilayered decision making necessary for AI, data and robotic systems operating in complex environments.

Action and interaction covers the challenges of human interaction, machine to machine inter-operation and machine interaction with the human environment.

These multiple forms of action and interaction create complex challenges that range from the optimisation of performance to physical safety and social interaction with humans in unstructured and multi-faceted environments.

Systems, hardware, methods and tools provide the technologies that enable the construction and configuring of systems, whether they are built purely on data or on autonomous robots.

These tools, methods and processes integrate AI, data and robotics technologies into systems and are responsible for ensuring that core system properties and characteristics such as safety, robustness, dependability and trustworthiness can be integrated into the design cycle, tested, validated and ultimately certified for use.

Each technical area overlaps with the other, there are no clear boundaries. Indeed, exciting advances are most often made in the intersections between these five areas and the system level synergies that emerge from the interconnections between them.

Author: Edward Curry (NUIG), Barry O’Sullivan (UCC) 

Reference

1.) Communication Artificial Intelligence of 25th  April 2018 (see https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe) and Communication Artificial Intelligence of December 7, 2018 ( see https://ec.europa.eu/commission/news/artificial-intelligence-2018-dec-07_en)