This chapter delves into the concept of paradata with the dual aim to link paradata’s notable complexity to broad utility, and to provide groundwork for exploring paradata in the subsequent chapters of this volume. The concept of paradata is investigated in several ways, including explaining the etymology of paradata and reviewing paradata definitions in survey research, archaeology, and heritage visualisation research – three domains where paradata use is most well-established. The chapter then moves on to discuss metadata and provenance data, two key related terms that are used to further interrogate the concept of paradata. The chapter shows that the concept of paradata encompasses a range of meanings and definitions, and that these share several common characteristics and correspondences, but also notable differences. To conclude, the chapter outlines two approaches to grasping and utilising the concept of paradata. The many definitions of and approaches to paradata are discussed as being, in some respects, an obstacle for understanding and employing the concept. The chapter, however, also underlines the complexities of the concept of paradata as a useful resource when building connectivities across its many possible domains of use and application.

Foreword from a preeminent retired High Court of Australia judge

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.

Several criminal offences can originate from or culminate with the creation of content. Sexual abuse can be perpetrated by producing intimate material without the subject’s consent, while incitement to criminal activity can begin with a simple conversation. When the task of generating content is entrusted to artificial agents, it becomes necessary to delve into the associated risks posed by this technology. Generative AI changes criminal affordances because it simplifies access to harmful or dangerous content, amplifies the range of recipients, creates new kinds of harmful content, and can exploit cognitive vulnerabilities to manipulate user behaviour. Given this evolving landscape, the question that arises is whether criminal law should be involved in the policies aimed at fighting and preventing Generative AI-related harms. The bulk of criminal law scholarship to date would not criminalise AI harms on the theory that AI lacks moral agency. However, when a serious harm occurs, responsibility needs to be distributed considering the guilt of the agents involved, and, if it is lacking, it needs to fall back because of their innocence. Legal systems need to start exploring whether and how guilt can be preserved when the actus reus is completely or partially delegated to Generative AI.

This chapter deals with the use of Large Language Models (LLMs) in the legal sector from a comparative law perspective, exploring their advantages and risks, the pertinent question as to whether the deployment of LLMs by non-lawyers can be classified as an unauthorized practice of law in the US and Germany, what lawyers, law firms and legal departments need to consider when using LLMs under professional rules of conduct - especially the American Bar Association Model Rules of Professional Conduct and the Charter of Core Principles of the European Legal Profession of the Council of Bars and Law Societies of Europe, and, finally, how the recently published AI Act will affect the legal tech market – specifically, the use of LLMs. A concluding section summarizes the main findings and points out open questions.

Making sense of paradata as information on practices and processes is both a matter of theory and practice. This chapter introduces a comprehensive theoretical reference model for paradata and discusses its practical implications. Paradata is approached as a category of things that can be appropriated as being informative about processes and practices. Working knowledge on practices and processes, and the practices and processes themselves, can create paradata through both embodiment and acts of inscription. Paradata turns back to working knowledge through appropriation. Enactment turns paradata back to practices and processes. Paradata materialises as a process and network-like meshwork in space-time. It is perpetually in the making and stabilised momentarily only at times when it is taken into use.

Paradata is a concept that is very much in the making. Its significance is not given and it can matter in different ways depending on context and how the notion itself is operationalised in use. Paradata complements earlier metainformation concepts for knowledge organisation in how it can facilitate systematising and making the complexity of data, practices and processes visible. As a mindset, paradata underlines the importance of being involved both in the theory and practice of how data is constantly being made and remade. There are, however, practical and ethical limits to what paradata can do and how far, and where are the limits of what is desirable to do with it. Ultimately, mastering the use of paradata and making it matter is also a question of literacy, tightly interwoven in the intricate meshwork of the social reality of the domains where it is put to work.

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.

Generative AI promises to have a significant impact on intellectual property law and practice in the United States. Already several disputes have arisen that are likely to break new ground in determining what IP protects and what actions infringe. Generative AI is also likely to have a significant impact on the practice of searching for prior art, creating new materials, and policing rights. This chapter surveys the emerging law of generative AI and IP in the United States, sticking as close as possible to near-term developments and controversies. All of the major IP areas are covered, at least briefly, including copyrights, patents, trademarks, trade secrets, and rights of publicity. For each of these areas, the chapter evaluates the protectability of AI-generated materials under current law, the potential liability of AI providers for their use of existing materials, and likely changes to the practice of creation and enforcement.

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.

This chapter will focus on how Chinese and Japanese copyright law balance content owner’s desire for copyright protection with the national policy goal of enabling and promoting technological advancement, in particular in the area of AI-related progress. In discussing this emerging area of law, we will focus mainly on the two most fundamental questions that the widespread adoption of generative AI pose to copyright regulators: (1) does the use and refinement of training data violate copyright law, and (2) who owns a copyright in content produced by or with the help of AI?