Secrecy in science is the subject of a series of papers in the latest issue of the Duke University Law School journal Law and Contemporary Problems. The authors consider the consequences of secret science and “propose solutions to help balance the costs and benefits of such secrecy.”
See a descriptive news release here.
The full text of the special issue on “Sequestered Science,” edited by David Michaels and Neil Vidmar, is here.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.