In Memory of Jeremy J. Stone
Jeremy J. Stone was president of the Federation of American Scientists from 1970 to 2000, and an influential figure in the history of arms control, human rights, and public interest advocacy.
Jeremy was remembered by colleagues and friends at a gathering in Washington, DC on April 30, 2017. Speakers included:
- Alton Frye (introductory and concluding remarks)
- Richard L. Garwin
- Michael Mann
- Fran Armstrong
- Saule Tuganbaeva
Obituaries
- Jeremy Stone, Who Influenced Arms Control During Cold War, Dies at 81 by Richard Sandomir, New York Times, January 5, 2017
- Jeremy Stone, arms-control advocate who led activist science group, dies at 81 by Matt Schudel, Washington Post, January 5, 2017
- Jeremy J. Stone, 1935-2017 by Steven Aftergood, Secrecy News, January 5, 2017
Catalytic Diplomacy
Following his tenure at FAS, Jeremy created a new organization called Catalytic Diplomacy, from which he launched new initiatives in conflict resolution.
His lively and fascinating memoir, Every Man Should Try: Adventures of a Public Interest Activist can be downloaded for free from the Catalytic Diplomacy website.
Biography
For biographical information, see this Wikipedia page on Jeremy.
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.