This chapter applies the conceptual framework developed in Chapter 2 to franchise disclosure as a concrete regulatory case study. Franchising is a major economic sector governed by a federal disclosure regime and involves investment decisions that require substantial commitments of capital and time. The chapter demonstrates that, despite its formal structure, the current franchise disclosure system performs poorly when assessed against contemporary technological standards. It exhibits low levels of big data readiness and performs weakly under artificial intelligence based linguistic evaluation. The chapter proceeds by first introducing the franchise sector and explaining why it provides a particularly revealing setting for assessing disclosure quality. It then examines the federal franchise disclosure framework, including its policy rationale, legislative history, substantive disclosure requirements, and comparative perspectives from other jurisdictions. Building on this foundation, the chapter concludes with a systematic technology based evaluation of franchise disclosures, assessing database accessibility, machine readability, and linguistic quality, and highlighting the limitations of existing disclosure design when examined through modern data practices.

This chapter develops a technology centered framework for evaluating the quality of legally mandated disclosures in contemporary information environments, proceeding from the premise that disclosure quality, understood as the extent to which disclosures enable informed decision making by consumers and investors, is central to the effectiveness and legitimacy of disclosure based regulation. It argues that traditional non technological assessment methods, including page length, disclosure volume, classic readability formulas, and the identification of legal jargon, are increasingly inadequate in light of advances in big data and artificial intelligence. The chapter first examines the conceptual and practical limitations of conventional approaches to disclosure quality, then introduces the data intelligence revolution, highlighting key developments in big data and artificial intelligence that have transformed how information can be processed, analyzed, and communicated. It next demonstrates how these technologies can enhance disclosure quality at both the macro level, through large scale comparative analysis by data driven intermediaries, and the micro level, through artificial intelligence tools that improve readability, conciseness, and linguistic accessibility. Finally, the chapter proposes a novel evaluative framework grounded in technological capacity, centered on big data readiness and artificial intelligence driven linguistic standards, offering a forward looking basis for assessing disclosure quality in the digital age.

Machine-readable humanity is an evocative idea, and it is this idea which Hanley et al. spell out and critically discuss in their contribution. They are interested in exploring the technological as well as the moral side of the meaning of machine-readability. They start by differentiating between various ways to collect (and read) data and to develop classification schemes. They argue that traditional top-down data collection (first the pegs and then the collection according to the pegs) is less efficient than more recent machine readability, which is dynamic, because of the successive advances of data and predictive analytics (“big data”), machine learning, deep learning, and AI. Discussing the advantages as well as the dangers of this new way to read humans, they conclude that we should be especially cautious vis-à-vis the growing field of digital biomarkers since in the end they could not only endanger privacy and entrench biases, but also obliterate our autonomy. Seen in this light, apps (like AdNauseam) that restrict data collection as a form of protest against behavioral profiling also constitute resistance to the inexorable transformation of humanity into a standing reserve: humans on standby, to be immediately at hand for consumption by digital machines.