Abstract

The European Union (EU) Commission’s whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society.

Highlights

  • The European Union Commission’s whitepaper (ECWP) on Artificial Intelligence (AI) anticipates a common market for AI and aims to secure civil rights in a future EU society embedded with AI systems (EC EUWP 2020; MSINET 2018)

  • Following the model of Lehtola and Ståhle (2014), we argue that the market-driven AI is a successful societal innovation, i.e., it is successfully embedded into the society, when the balancing of economic factors against the symbolic factors in Table 2 succeeds

  • Our analysis identifies two main gaps between what may be essential for the success of the European Commission (EC) plan, and what is provided by this proposal

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Summary

Introduction

The European Union Commission’s whitepaper (ECWP) on Artificial Intelligence (AI) anticipates a common market for AI and aims to secure civil rights in a future EU society embedded with AI systems (EC EUWP 2020; MSINET 2018). By adopting such a wide AI definition, the EC aims for providing a general framework to regulate different types of AI without, focusing on specific and contextual details Embracing such a general perspective on AI sets a clear limitation to the present work, namely, that we can only marginally discuss specific implementations of such systems and ethical analysis of these solutions are beyond the scope of this manuscript. This centrality of people rights for AI development is connecting the EC plan with the UN declaration of Human Rights (UN General Assembly 1948; UNESCO 2021) This human-centric approach is a key difference with, for instance, the US National Science Technology Council (NSTC) which mainly intends AI as a set of transformative technologies putting at the centre the value of these in terms of social and economic empowerment (NSTC 2019). Our goal is to identify challenges and opportunities and suggest future directions for the embedding of AI in our society, and we discuss these towards the end of the article

Status quo AIHLEG guidelines and the EU values driving manufacturers
The gaps in the European vision on AI through the culture cycle framework
Societal integration of AI
State and AI
Civil society and AI
Tailored utopia and hidden Taylorism
International corporations and AI
Future society: how the EU may avoid an AI crisis
Technical challenges and opportunities
Training challenges in the era of deep learning
AI data storage and maintenance
Explainable AI: what does it do and why?
Looking for consensus on practices for interaction between humans and AI
The future of interaction with AI systems
Findings
Conclusion
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