Jasanoff coined “technologies of hubris” as the foil to “technologies of humility” in a 2003 essay in Minerva titled “Technologies of Humility: Citizen Participation in Governing Science.” The essay emerged from her decades of comparative research on how regulatory institutions in different national contexts produced knowledge about technology risks. In each case, she observed the same structural pattern: institutions built evidentiary frameworks that admitted certain kinds of knowledge and excluded others, and the excluded knowledge was systematically the knowledge of people who experienced the consequences of technology rather than the people who built it.
The concept of hubris here is not an accusation of arrogance directed at individual practitioners. Risk assessors and cost-benefit analysts are often genuinely humble people. The hubris is institutional and structural: it is built into frameworks that produce the appearance of certainty where genuine uncertainty exists, that transform the qualitative into the quantitative not because the transformation is valid but because the transformed product is actionable within existing institutional architecture. A risk matrix with probability estimates attached can be incorporated into a regulatory decision. A narrative account of how a technology is changing the felt quality of professional life cannot.
The AI moment has produced an extraordinary proliferation of technologies of hubris under the banner of “AI safety.” Safety benchmarks quantify a model's tendency to produce harmful outputs across specified categories. Alignment protocols measure the distance between a model's behavior and its designers' intentions. Red-teaming exercises systematically probe for failure modes. Each of these is a genuine technology of hubris: it produces real knowledge about specific, measurable properties of the system, and it systematically excludes the consequences that cannot be measured with the tools it employs.
The probability-uncertainty substitution. The central operation of technologies of hubris is the substitution of measurable risk for unmeasurable uncertainty. Risk is a probability distribution over outcomes; uncertainty is the condition in which no such distribution can be reliably constructed. Real-world technology deployment almost always involves genuine uncertainty rather than quantifiable risk, because the consequences of a new technology arise from its interaction with a social order that cannot be fully modeled. Technologies of hubris transform this uncertainty into risk through a series of assumptions, and then govern the risk as though the assumptions were verified. The governance gap is partly a product of this substitution: institutions calibrated to manage quantifiable risk are not designed to handle genuine uncertainty.
The actionability constraint. Technologies of hubris persist not because they are epistemically superior but because they produce knowledge in a form that existing institutions can act on. A finding that “AI increases cognitive load by 23%” can be translated into a workplace standard. A finding that “sustained AI use is reorganizing the felt quality of professional identity in ways that accumulate below the threshold of conscious awareness” cannot. The evidentiary architecture of governance institutions selects for the actionable, and the actionable is the quantitative, and the quantitative is the measurable, and the measurable is almost never the most important.
The exclusion of experiential knowledge. Technologies of hubris are epistemically exclusive by design: they establish standards of evidence that the expert can meet and the citizen cannot. The engineer knows the specifications. The resident near the facility knows what it smells like at 3 a.m. and what it has done to her family's health. In every governance domain Jasanoff has studied, including AI, the former knowledge is admissible and the latter is not—not because the latter is less true but because the existing institutional architecture was built to process the former. The epistemic exclusion is structural, not intentional.
Contrast with technologies of humility. The prescriptive counterpart, technologies of humility, are institutional practices designed to operate inside genuine uncertainty rather than against it. They attend to framing (who defines the problem), vulnerability (who is most exposed to harm), distribution (who benefits and who pays), and learning (how institutions detect their own errors). The point is not to abandon technologies of hubris but to surround them with technologies of humility that supply what quantitative frameworks cannot: the knowledge of people who are experiencing the consequences, the recognition of what cannot be measured, and the institutional capacity to revise when reality diverges from the model.