AI Practice Framework (Klein Reading)

The AI Practice Framework in Klein's reading is a structural set of design principles for organizations deploying AI systems while preserving the conditions under which human expertise develops and is maintained. The framework comprises five principles derived from four decades of research on expertise: deliberate exposure to the raw domain, in which practitioners engage with the domain's phenomena without AI mediation on a regular basis; structured failure exposure, in which users are deliberately presented with AI failure modes to build pattern libraries for error detection; preservation of social cognitive infrastructure, in which mentoring relationships, team debriefs, and in-person processes are maintained against efficiency pressure; expertise auditing, in which organizations regularly assess whether human expertise is being maintained, developed, or degraded by AI deployment; and explicit leadership decision-making about the level of human expertise the organization requires. The framework is demanding because it imposes costs — manual practice is slower than AI-assisted production, in-person processes are less efficient than AI-mediated alternatives — but its structural argument is that these