Comments – ML-Draft-018

This introduction clearly establishes epistemic integrity as the foundation for trustworthy interaction. As the chapter develops, it may be useful to include examples of how participants form "reliable beliefs" in practice, particularly in situations where decisions depend on rapidly changing information. Concrete examples could help readers connect the principle to real-world applications while keeping the property broadly applicable.

The relationship between DP14 and DP15 is well explained. It may also be worth emphasizing that transparency is not only about exposing evidence but also about presenting evidence in a way that is understandable and actionable for participants who are not technical experts.

This introduces an important challenge. The chapter might later expand on how interfaces can help participants understand not only what is true, but also who is making a claim, what evidence supports it, when it was last verified, and whether important uncertainty remains

As AI-generated explanations become more common, it may be helpful to distinguish between explanations that summarize verified evidence and those that infer or generate new conclusions. Transparency should help participants understand whether an explanation is evidence-based, inferential, or generative, so they can better assess its reliability

The phrase "socially deceptive" is powerful and captures an important concept. It may be helpful later in the chapter to briefly clarify how technically correct systems can still produce socially deceptive outcomes through selective presentation, missing context, interface design, or incomplete disclosure. A concrete example could make this distinction easier for readers to understand.

This draft provides a strong conceptual foundation for epistemic integrity by focusing on the conditions that allow participants to form reliable beliefs about system behavior. One opportunity for the chapter is to illustrate these principles through practical, high-consequence examples where trust and transparency directly influence decision-making, such as public safety, emergency coordination, healthcare, or critical infrastructure. These examples would help demonstrate that epistemic integrity is not only about preventing deception, but also about enabling participants to confidently understand who issued information, what evidence supports it, how current it is, and what uncertainties remain. Such examples could strengthen the chapter while preserving its broad applicability across many Meta-Layer use cases.