CONCEPT
Progressive Rationality of AI Adoption
The operational framework for rational AI adoption that emerges from Laudan's analysis: <em>conditional commitment</em>, acknowledgment of residual problems, continuous evaluation, and distributed epistemic responsibility — not a position but a practice.
Laudan's framework does not prescribe a policy. It constrains the space of rational responses by eliminating the positions that fail the problem-solving test. What remains is a set of features a progressive rationality of AI adoption would exhibit. First, conditionality: adoption is specified against conditions under which it is progressive, with ongoing evaluation of whether those conditions are being met. Second, acknowledgment of residual problems as obligations: the displaced are owed institutional responses, not aggregate consolations. Third, continuous evaluation: the framework is a practice, not a verdict, maintained through the permanent willingness to revise as evidence accumulates. Fourth, distributed epistemic responsibility: the work of evaluation belongs to every institution and individual the transition affects, not to a single class of experts whose authority is presumed. The rationality is neither adoption nor rejection. It is disciplined engagement, sustained across decades.
In The You On AI Field Guide
Conditionality is the first feature because it is the feature both the triumphalist and elegist traditions tend
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