Regulatory Science

Regulatory science is Jasanoff's term for the distinctive form of knowledge produced when scientific methods are applied within regulatory contexts. It is not pure science (conducted for the sake of understanding) or applied science (using established knowledge to solve practical problems) but a third category: science conducted under the constraints of institutional decision-making, where the purpose is not discovery but actionable conclusions about safety, efficacy, or risk. Regulatory science operates under different epistemic standards than academic science — certainty must be produced on institutional timelines, evidence must be legible to non-specialists, and conclusions must be defensible in adversarial contexts. The knowledge it produces is real and valuable but is shaped by the regulatory framework as much as by the phenomena being studied. Applied to AI, regulatory science explains why governance frameworks struggle: the consequences that matter most (identity erosion, cognitive atrophy, meaning displacement) resist the quantitative certainty that regulatory institutions require.