Artificial intelligence (AI) now permeates critical infrastructures and decisionmaking systems where failures produce social, economic, and democratic harm. This position paper challenges the entrenched belief that regulation and innovation are opposites. As evidenced by analogies from aviation, pharmaceuticals, and welfare systems and recent cases of synthetic misinformation, bias and unaccountable decision-making, the absence of well-designed regulation has already created immeasurable damage. Regulation, when thoughtful and adaptive, is not a brake on innovation—it is its foundation. The present position paper examines the EU AI Act as a model of risk-based, responsibility-driven regulation that addresses the Collingridge Dilemma: acting early enough to prevent harm, yet flexibly enough to sustain innovation. Its adaptive mechanisms—regulatory sandboxes, small and medium enterprises (SMEs) support, real-world testing, fundamental rights impact assessment (FRIA)—demonstrate how regulation can accelerate responsibly, rather than delay, technological progress. The position paper summarises how governance tools transform perceived burdens into tangible advantages: legal certainty, consumer trust, and ethical competitiveness. Ultimately, the paper reframes progress: innovation and regulation advance together. By embedding transparency, impact assessments, accountability, and AI literacy into design and deployment, the EU framework defines what responsible innovation truly means—technological ambition disciplined by democratic values and fundamental rights.

Eu-LISA is launching the European Travel Information and Authorization System (ETIAS), which seems an example of a different, human rights-oriented approach to AI within the law enforcement. However, the reality is quite different: the usual problems of the use of AI—lack of transparency, bias, opacity, just to name a few—are still on board. This paper critically assesses these promises of ETIAS and argues that it has serious issues that have not been properly dealt with. So as to argue the need to address these issues, the paper addresses ETIAS within the wider context of human rights and solidarity-based data governance. In this respect, ETIAS is seen as a tool which uses data for high value purposes, such as EU safety and security, yet it also calls for serious risk mitigation measures. Indeed, the risks related to law enforcement on the borders and in migration management are extremely serious due to the vulnerability of people who escape from poverty, wars, regimes, and other disasters. In the third part of this article, we articulate three proposals of such risk mitigation measures. We argue in favour of strengthening critical general safeguards in ETIAS, then elaborate a principle that should guide AI-based public service development (P4P principle) and end with a few IPR-related requirements for private sector involvement in such services. Adopting these measures could contribute to reduce the risk of building EU AI expertise upon data coming from the most vulnerable social groups of our planet.

This is a paper about the need for algorithmic rights: the right to algorithmic transparency and algorithmic explainability; the right to be informed if we are interacting with an automated system; the right not to be subject to a decision based solely on automated processing; and the right not to depend on an algorithmic system for compliance with our fundamental rights, but to instead choose human interaction in ensuring such compliance. There are several angles from which these rights can be discussed, but I will be looking at them as tools of empowerment supporting the right to good administration in the EU, where they can help us meet the challenge of good governance in an algorithm-based and data-driven public sector, and can in this way help to strengthen the rule of law and the fundamental rights that are central to it.

The idea of the legal personhood of artificial intelligence (AI) --- the idea that intelligent agents can have rights and incur obligations under the law--- is controversial, and in fact is often dismissed out of hand: in this paper I will argue that, on the contrary, such legal personhood may be the next big challenge for our legal systems, and we need it to deal with the new kinds of complexity introduced by AI. Furthermore, I argue that we already have experiences we can look: to this end we can draw on the reasoning applied to the legal personhood recognized for corporations and other nonhuman entities. In order to do this, I address some of the criticisms against ascribing legal personhood to AI. I also look at the Canadian and EU ethical guidelines so as to keep the development of AI within the framework of human values, and I show that an ascription of legal personhood to AI is consistent with them. I also address a few of the big issues involved in making the legal personhood of AI a reality.

In this paper we present an application of argument maps for assessing liability in the field of Air Traffic Management (ATM), developed within the ALIAS (Addressing the Liability Impact of Automated Systems) project. Such maps are used for presenting legal concepts and norms to lawyers and non lawyers (engineers, software developers and other technical personnel), within the cooperative design and assessment of new technologies for ATM.

This paper analyzes the representation of legal knowledge, and of Intellectual Property (or IP) law in particular. This is done by looking at two ways of representing the IP domain: through an IP ontology and by way of rule representation. And although these two solutions differ in many respects, they share the conceptual problems specific to this domain. This paper is based on research conducted under the EU-funded project ALIS (IST-2004-2.4.9).