Trust Is Designed, Not Assumed

What this chapter covers

Trust in AI systems is not a feeling. It is a set of properties users, executives, and regulators can verify. This chapter unpacks those properties and shows how leaders design for them from day one instead of retrofitting them after the first incident.

Key takeaways

• The five verifiable properties of a trusted AI system
• How to build a trust story an executive can retell in one meeting
• The role of explainability, evaluation, and observability in earning trust
• How to communicate risk without killing momentum
• A trust scorecard you can run on any AI project