AI made in Europe? Five theses for the development of a European AI ecosystem

The EU Commission has presented two important publications: in addition to the European Data Strategy – the first data strategy Europe has ever had – the AI White Paper was presented as a regulatory framework addressing the ethical and societal implications of artificial intelligence. It is no coincidence that both publications are published at the same time; on a global level, a clear political signal could be sent: the development and application of AI in Europe is first of all unimaginable without data, but secondly also without precise ethical rules and values. The publication of the data strategy and the white paper make it clear, especially at the global political level, that ethical issues such as trust, acceptance and transparency are significantly shaping contemporary AI discourse. The question of what artificial intelligence can do is thus at least as important as the question of what it is allowed to do at all.

As an experienced AI company, Retresco operates in the area of tension between technology, business and society, and is always aware of the ethical implications of corporate actions. Current advances in data policy and AI regulation provide a welcome framework in this respect. Alexander Siebert and Johannes Sommer, CEOs of Retresco, comment: ‘A thriving AI ecosystem in Europe can only be an ecosystem of trust. The European Commission has made this clear. They have signaled that a European AI cannot be conceived without a functioning data infrastructure on the one hand, of course, or without an ethical framework on the other. At the same time, however, care should be taken to ensure that funding measures at EU level are always harmonised with respective national strategies and mesh in a scalable manner. It is only in this way that a holistically integrated data and AI-funding landscape in Europe will be able to develop to its full effectiveness. And this is precisely where a weakness lies in the EU Commission’s plans: the statement “AI made in Europe” or “market leadership in AI technology” is in itself neither a goal nor an overarching vision, because the multifaceted nature of AI applications is far too broad. What do we want to be? Market leaders in autonomous vehicle technology? Healthcare? Internet of things? Or would it even make sense for individual countries to choose a specific AI focus? Only with focus and concrete measures will Europe succeed in establishing an effective AI strategy’.

The following theses and recommendations for action regarding an ‘AI made in Europe’ should represent a possible starting point for sustainably promoting the development and implementation of AI technologies in Europe.

1. AI as a key technology

Artificial intelligence is not a new technology among many others; it is a key technology. As such, it fundamentally changes the way we work, operate and live together as a society. These developments will intensify in the future. At the macro level, artificial intelligence will have a major impact on economic and social progress. The AI investments of 20 billion euros per year now announced by the EU Commission appear far too small in this context. For comparison: Amazon alone invested a total of 35.9 billion dollars in research and development in 2019.

2. Ethical values as a guiding principle

Fairness, transparency, security, inclusion and accountability are ethical aspects that are indispensable when it comes to building a trustworthy and people-centred AI. In this way, the full potential of artificial intelligence can be exploited and the bridge between technology, economy and society can be built.

3. Multilateral funding

In order to strengthen the European AI ecosystem, the application-oriented research & development of AI technologies should be promoted in a more targeted manner, especially in comparison with the US or China. However, a multi-perspective approach is desirable here: universities and industry should be able to work more closely together, for example in the form of AI hubs and real laboratories. In addition, the data infrastructure should be substantially improved through a sound data strategy and the socio-cultural acceptance of AI should be increased through targeted educational measures. Multilateral funding should also mean, however, that individual national AI strategies should be integrated into each other so that a holistically harmonised AI funding landscape in Europe can be effective.

4. More eagerness to experiment

A change of mentality seems essential. With regard to the practical application of artificial intelligence, we need more adventurousness, more courage to take risks and more joy in experimentation. Above all, however, we need a positive error culture that allows us to learn from mistakes without the need for ad hoc comprehensive regulation.

5. Promoting acceptance by society as a whole

In public AI discourses, the unilateral emphasis on only the opportunities or only the risks of AI should be avoided. Instead, a transparent social discourse which allows for contextual grey areas and openly addresses them should be established. Public education with a factual, objective basic tone is indispensable to increased social acceptance of AI.

As a key technology, artificial intelligence will significantly shape not only business processes but also economic and social progress. Economic potential plays just as important a role as social responsibility and ethical challenges. Therefore, a multilateral cooperation of representatives from economy, politics and society seems to be all the more important – only together can AI applications be designed according to public welfare standards and technological progress be accelerated sustainably.