ABSTRACT In Power Without Knowledge, Jeffrey Friedman develops a critique of social science to argue that current technocratic practices are prone to predictive failures and unintended consequences. However, he does not provide evidence that the cause he singles out—“ideational heterogeneity”—is in fact a non-negligible source of technocratic limitations, more than or alongside better-known problems such as missing data, measurement issues, interpretive difficulties, and researcher bias. Even if we grant ideational heterogeneity, Friedman’s preferred institutional solution of exitocracy does not necessarily follow. (...) His critical epistemology would also be compatible with radical forms of collective action. (shrink)

The catastrophic risks posed by new technologies such as killer robots and geoengineering have triggered calls for halting new research. Arguments for restricting research typically have a slippery‐slope structure: Researching A will lead to deployment; we have decisive moral reasons against deployment; therefore, we should not research A. However, scientific uncertainty makes it difficult to prove or disprove the conclusion of slippery‐slope arguments. This article accepts this indeterminacy and asks whether and when it would be permissible to restrict research under (...) empirical and normative uncertainty. The argument starts from the ethical framework for the regulation of scientific research with human subjects and offers modifications to adapt it to the purpose of restricting new technologies. Two main questions arise in the process: whether it is permissible to impose restrictions at the research stage to prevent harms that will arise from the use of a technology and whether it is permissible to restrict research preemptively on the grounds of public fear and anxiety, before there is sufficient evidence establishing the risk of harm. I answer both questions in the affirmative and defend this position against objections. (shrink)

Will existing forms of artificial intelligence (AI) lead to genuine intelligence? How is AI changing our society and politics? This essay examines the answers to these questions in Brian Cantwell Smith's The Promise of Artificial Intelligence and Mark Coeckelbergh's The Political Philosophy of AI with a focus on their central concern with judgment—whether AI can possess judgment and how developments in AI are affecting human judgment. First, I argue that the existentialist conception of judgment that Smith defends is highly idealized. (...) While it may be an appropriate standard for intelligence, its implications for when and how AI should be deployed are not as clear as Smith suggests. Second, I point out that the concern with the displacement of judgment in favor of “reckoning” (or calculation) predates the rise of AI. The meaning and implications of this trend will become clearer if we move beyond ontology and metaphysics and into political philosophy, situating technological changes in their social context. Finally, I suggest that Coeckelbergh's distinctly political conception of judgment might offer a solution to the important boundary-drawing problem between tasks requiring judgment and those requiring reckoning, thus filling a gap in Smith's argument and clarifying its political stakes. (shrink)

Rachel Friedman’s Probable Justice and Jeffrey Friedman’s Power without Knowledge explore the promises and pitfalls of the application of predictive tools to the solution of social and political problems. Rachel Friedman argues that a fundamental duality in philosophical interpretations of probability allowed social insurance schemes to successfully accommodate two rival visions of liberal justice over the centuries. But in focusing on ideas around probability, she misses the limitations of the experts who put these ideas into practice and threatened to undermine (...) them in the process. Jeffrey Friedman, by contrast, is centrally concerned with the limitations of experts. While he shows how these undermine one rather narrow conception of technocratic legitimacy, he avoids examining their implications for democratic legitimacy understood more broadly. (shrink)

Rachel Friedman’s Probable Justice and Jeffrey Friedman’s Power without Knowledge explore the promises and pitfalls of the application of predictive tools to the solution of social and political problems. Rachel Friedman argues that a fundamental duality in philosophical interpretations of probability allowed social insurance schemes to successfully accommodate two rival visions of liberal justice over the centuries. But in focusing on ideas around probability, she misses the limitations of the experts who put these ideas into practice and threatened to undermine (...) them in the process. Jeffrey Friedman, by contrast, is centrally concerned with the limitations of experts. While he shows how these undermine one rather narrow conception of technocratic legitimacy, he avoids examining their implications for democratic legitimacy understood more broadly. (shrink)