In the Feyerabend lecture Kant already presents his claim that the principle of right is a principle of coercion, that is, that the state is authorized to use coercion to counteract an unauthorized violation of universal freedom. Such state use of force is a hinderance of a hinderance to freedom. But how is this coercive power specified in particular circumstances? I examine three extreme cases in which a state might be authorized to use its coercive power against its own citizens to cause their deaths: capital punishment, eminent right in emergencies, and war. This paper will show that Kant offered specific explanations of particular limits to legitimate state power, rejecting different limits offered by Beccaria (capital punishment), Achenwall (eminent right and war), and Vattel (war). These assessments reveal that Kant was of several minds regarding whether in any social contract a citizen could rationally consent to these uses of coercion and whether actual or only hypothetical consent was required. I suggest that only later in the published Doctrine of Right did Kant work out his position consistently.

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.

We treat some more advanced topics: monstrous (and other) moonshine, Monster and E_8, Niemeier lattices, the triangle property, generalized line graphs, quiver representations, cluster algebras, von Neumann algebras, catastrophes, Calabi–Yau, elliptic fibrations.

Iraq. Met with Kofi Annan today. We agreed that the interview with Pentagon hawk Wolfowitz today in the NY Times signalled that the idea of an armed attack on Iraq was shelved for the time being. We also agreed that this signal from Wolfowitz meant that there would be no inspections in the spring. Why should Iraq accept inspections if it were not to be a way of avoiding invasion?

For the US, the course apparently decided on meant that they would be good multilateralist boys for a while, insisting on the implementation of Res. 1284 including, notably, inspections. The Arab states will support that, feeling that the US had met their concerns and refrained from early military action against Iraq.

Ten case studies form the empirical and analytical core of the book. Chapter 1 introduces these cases, detailing the arduous journeys marginalised workers and communities pursue in seeking redress for grievances arising from harmful business practices. Their aims vary, some wanting to improve working conditions or pursue compensation for past wrongs, others attempting to block planned business projects or create pressure for broader change to prevent recurring patterns of human rights abuse. Their efforts, together with worker and civil society allies, to gain meaningful outcomes are marked by creativity and diversity in the sheer multitude of methods utilised. Critically, transnational NJMs are only one avenue they pursue. Despite their vast efforts, significant human rights breaches persist alongside small victories. The cases provide compelling evidence that NJMs are best understood as but one actor within broader systems of transnational business regulation.

This chapter examines a series of events that occurred in Chuquisaca in mid-1781 following the spread of rumors of an alleged popular revolt against rising royal taxes. While there was no compelling evidence of such a conspiracy, the focal point of discord became the origin and intent of the false rumors, which the urban resident, both patricians and plebeians, attributed the peninsular audiencia ministers and a newly arrived company of Spanish soldiers dispatched from Buenos Aires to suppress the pan-Andean rebellion led by Tupac Amaru and Tupac Katari. At the root of the public conflict was a dispute over the status of the Chuquisaca people within the monarchy following their victory over the indigenous insurgents. Fears of a popular uprising ended up giving rise to novel forms of group representation, political rituals that underscored the sudden relevance acquired by the opinions of the local population, and public ceremonies that exposed the construction of the city as a subject of history and a political actor. In the customary language of monarchical legitimism, the collective practices both invoked and subverted the axiomatic loyalty to the Crown.

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.

While generative AI enables the creation of diverse content, including images, videos, text, and music, it also raises significant ethical and societal concerns, such as bias, transparency, accountability, and privacy. Therefore, it is crucial to ensure that AI systems are both trustworthy and fair, optimising their benefits while minimising potential harm. To explore the importance of fostering trustworthiness in the development of generative AI, this chapter delves into the ethical implications of AI-generated content, the challenges posed by bias and discrimination, and the importance of transparency and accountability in AI development. It proposes six guiding principles for creating ethical, safe, and trustworthy AI systems. Furthermore, legal perspectives are examined to highlight how regulations can shape responsible generative AI development. Ultimately, the chapter underscores the need for responsible innovation that balances technological advancement with societal values, preparing us to navigate future challenges in the evolving AI landscape.

The purpose of this chapter is to show how and where paradata emerges ‘in the wild’ of the many varieties of research documentation produced during scholarly work, and to demonstrate what this paradata might look like. The examination of paradata in research documentation is approached using perspectives of data ‘as practice’ and data ‘as thing’, emphasising simultaneously that paradata is malleable and will manifest differently across contexts of data production and use, but also that paradata is a tangible data phenomenon with identifiable characteristics. This chapter draws empirically from an interview study of archaeologists and archaeological research data professionals (N=31). Theoretical framing is provided by scholarship on data and documentation. The chapter reveals how paradata in research documentation emerges in different forms and with varying scope, comprehensiveness and degrees of formalisation. It also suggests that there are technical and epistemic usefulness thresholds relevant for identifying and using paradata. The technical usefulness threshold represents baseline possibilities of accessing and interacting with paradata in research documentation. The epistemic usefulness threshold underlines instead the degree of affinity between the intellectual horizons of paradata creation and paradata use, and several resources are identified that can help to strengthen this affinity.

Kant’s Naturrecht Feyerabend lectures are contemporaneous to his Groundwork, which first sketches some key features of his Critical moral philosophy. Evidence of Kant’s Groundwork stands out when his lectures are compared to Achenwall’s Prolegomena and to Kant’s assigned text, Achenwall’s Ius naturae. Kant’s own Critical Rechtslehre, including his theory of property, develops much later, yet these lectures reveal several of Kant’s key issues and problems, his profound disagreements with traditional and contemporaneous natural law, some of his critical resources for radically improving philosophy of law. This chapter focuses on how Kant’s Critical issues and innovations pertain to individual rights to property.