Artificial Intelligence (AI) can collect, while unperceived, Big Data on the user. It has the ability to identify their cognitive profile and manipulate the users into predetermined choices by exploiting their cognitive biases and decision-making processes. A Large Generative Artificial Intelligence Model (LGAIM) can enhance the possibility of computational manipulation. It can make a user see and hear what is more likely to affect their decision-making processes, creating the perfect text accompanied by perfect images and sounds on the perfect website. Multiple international, regional and national bodies recognised the existence of computational manipulation and the possible threat to fundamental rights resulting from its use. The EU even moved the first steps towards protecting individuals against computational manipulation. This paper argues that while manipulative AIs which rely on deception are addressed by existing EU legislation, some forms of computational manipulation, specifically if LGAIM is used in the manipulative process, still do not fall under the shield of the EU. Therefore, there is a need for a redraft of existing EU legislation to cover every aspect of computational manipulation.

It is hard for regulation to keep up with the rapid development of new technologies. This is partly due to the lack of specialist technical expertise among lawmakers, and partly due to the multi-year timescales for developing, proposing and negotiating complex regulations that lag behind technological advances. Generative AI has been a particularly egregious example of this situation but is by no means the first. On the other hand, technical standardisation in global fora such as ISO and IEC generally does not suffer from a lack of specialist technical expertise. In many cases, it is also able to work on somewhat faster timescales than regulation. Therefore, many jurisdictions have developed synergistic approaches that combine the respective strengths of regulation and standardisation to complement each other.

The rise in the use of AI in most key areas of business, from sales to compliance to financial analysis, means that even the highest levels of corporate governance will be impacted, and that corporate leaders are duty-bound to manage both the responsible development and the legal and ethical use of AI. This transformation will directly impact the legal and ethical duties and best practices of those tasked with setting the ‘tone at the top’ and who are accountable for the firm’s success. Directors and officers will have to ask themselves to what extent should, or must, AI tools be used in both strategic business decision-making, as well as monitoring processes. Here we look at a number of issues that we believe are going to arise due to the greater use of generative AI. We consider what top management should be doing to ensure that all such AI tools used by the firm are safe and fit for purpose, especially considering avoidance of potential negative externalities. In the end, due to the challenges of AI use, the human component of top corporate decision-making will be put to the test, to prudentially thread the needle of AI use and to ensure the technology serves corporations and their human stakeholders instead of the other way around.

Generative AI offers a new lever for re-enchanting public administration, with the potential to contribute to a turning point in the project to ‘reinvent government’ through technology. Its deployment and use in public administration raise the question of its regulation. Adopting an empirical perspective, this chapter analyses how the United States of America and the European Union have regulated the deployment and use of this technology within their administrations. This transatlantic perspective is justified by the fact that these two entities have been very quick to regulate the issue of the deployment and use of this technology within their administrations. They are also considered to be emblematic actors in the regulation of AI. Finally, they share a common basis in terms of public law, namely their adherence to the rule of law. In this context, the chapter highlights four regulatory approaches to regulating the development and use of generative AI in public administration: command and control, the risk-based approach, the experimental approach, and the management-based approach. It also highlights the main legal issues raised by the use of such technology in public administration and the key administrative principles and values that need to be safeguarded.

This chapter examines the transformative effects of generative AI (GenAI) on competition law, exploring how GenAI challenges traditional business models and antitrust regulations. The evolving digital economy, characterised by advances in deep learning and foundation models, presents unique regulatory challenges due to market power concentration and data control. This chapter analyses the approaches adopted by the European Union, United States, and United Kingdom to regulate the GenAI ecosystem, including recent legislation such as the EU Digital Markets Act, the AI Act, and the US Executive Order on AI. It also considers foundational models’ reliance on key resources, such as data, computing power, and human expertise, which shape competitive dynamics across the AI market. Challenges at different levels—including infrastructure, data, and applications—are investigated, with a focus on their implications for fair competition and market access. The chapter concludes by offering insights into the balance needed between fostering innovation and mitigating the risks of monopolisation, ensuring that GenAI contributes to a competitive and inclusive market environment.

The chapter examines the legal regulation and governance of ‘generative AI,’ ‘foundation AI,’ ‘large language models’ (LLMs), and the ‘general-purpose’ AI models of the AI Act. Attention is drawn to two potential sorcerer’s apprentices, namely, in the spirit of J. W. Goethe’s poem, people who were unable to control a situation they created. Focus is on developers and producers of such technologies, such as LLMs that bring about risks of discrimination and information hazards, malicious uses and environmental harms; furthermore, the analysis dwells on the normative attempt of EU legislators to govern misuses and overuses of LLMs with the AI Act. Scholars, private companies, and organisations have stressed limits of such normative attempts. In addition to issues of competitiveness and legal certainty, bureaucratic burdens and standard development, the threat is the over-frequent revision of the law to tackle advancements of technology. The chapter illustrates this threat since the inception of the AI Act and recommends some ways in which the law has not to be continuously amended to address the challenges of technological innovation.

Generative AI has catapulted into the legal debate through the popular applications ChatGPT, Bard, Dall-E, and others. While the predominant focus has hitherto centred on issues of copyright infringement and regulatory strategies, particularly within the ambit of the AI Act, it is imperative to acknowledge that generative AI also engenders substantial tension with data protection laws. The example of generative AI puts a finger on the sore spot of the contentious relationship between data protection law and machine learning built on the unresolved conflict between the protection of individuals, rooted in fundamental data protection rights and the massive amounts of data required for machine learning, which renders data processing nearly universal. In the case of LLMs, which scrape nearly the whole internet, this training inevitably relies on and possibly even creates personal data under the GDPR. This tension manifests across multiple dimensions, encompassing data subjects’ rights, the foundational principles of data protection, and the fundamental categories of data protection. Drawing on ongoing investigations by data protection authorities in Europe, this paper undertakes a comprehensive analysis of the intricate interplay between generative AI and data protection within the European legal framework.

It is well-known that, to be properly valued, high-quality products must be distinguishable from poor-quality ones. When they are not, indistinguishability creates an asymmetry in information that, in turn, leads to a lemons problem, defined as the market erosion of high-quality products. Although the valuation of generative artificial intelligence (GenAI) systems’ outputs is still largely unknown, preliminary studies show that, all other things being equal, human-made works are evaluated at significantly higher values than machine-enabled ones. Given that these works are often indistinguishable, all the conditions for a lemons problem are present. Against that background, this Chapter proposes a Darwinian reading to highlight how GenAI could potentially lead to “unnatural selection” in the art market—specifically, a competition between human-made and machine-enabled artworks that is not based on the merits but distorted by asymmetrical information. This Chapter proposes solutions ranging from top-down rules of origin to bottom-up signalling. It is argued that both approaches can be employed in copyright law to identify where the human author has exercised the free and creative choices required to meet the criterion of originality, and thus copyrightability.