This Introduction provides a high-level overview of the subject matter and approach of the book. It introduces disclosure law as a central regulatory response to information asymmetries and briefly outlines the objectives commonly attributed to mandatory disclosure regimes. At an introductory level, the chapter notes persistent concerns regarding the practical effectiveness of disclosure requirements, particularly in digitally complex information environments. It outlines why traditional, non-technological measures of disclosure quality may be insufficient and why advances in big data analytics and artificial intelligence are relevant to contemporary evaluation of disclosure systems. The Introduction also explains the selection of franchising as the book’s principal case study and provides a brief overview of the structure of the chapters that follow. Its purpose is to orient the reader and define the scope of the inquiry, rather than to develop the book’s substantive analysis.

This chapter focuses on the institutional and policy implications of adopting a technology-centered approach to disclosure regulation. Rather than reassessing the empirical shortcomings of existing disclosure practices, it turns to the design of a forward-looking reform agenda. The chapter introduces the Tech Disclosure framework as a model for integrating data readiness and AI-oriented linguistic tools into the architecture of disclosure law. It then situates this framework in relation to leading reform proposals in the literature, clarifying how a technology-based approach differs from strategies grounded in simplification, scoring, personalization, or substantive prohibition. Finally, the chapter examines pathways for implementation, emphasizing the role of coordinated action by regulated entities and disclosure recipients in promoting regulatory modernization. Together, the analysis offers a policy roadmap for translating technological capacity into durable reform of disclosure regimes.

The summary chapter restates the book’s central arguments. It emphasizes that persistent critiques of disclosure law arise from the poor quality of disclosures rather than from the concept of disclosure itself. The chapter reiterates that traditional measures of disclosure quality, such as document length, readability scores, and reductions in legal jargon, are inadequate because they reflect paper-based assumptions and fail to account for digital conditions. It briefly recalls the franchise disclosure case study under the Federal Trade Commission’s Franchise Rule, which illustrated how existing disclosure regimes remain inaccessible to automated processing, insufficiently machine-readable, and burdened by verbosity. The chapter then summarizes the book’s proposed response, the Tech Disclosure model, which calls for redesigning disclosure infrastructure so that mandated information is digitally accessible, machine-readable, and linguistically clear when evaluated with AI tools.