This was a gripping, high-stakes read—thank you, Chara, for laying out the ethical tightrope of AI security research so vividly. The “transparency trap” isn’t just a theoretical dilemma anymore; it’s already shaping how we build, trust, and regulate AI systems. The RAS-Eval case especially exposes how academic intent can collide with real-world exploitation.
I appreciate the proposed solutions around staged disclosures and defensive-first publication standards. But I can’t help wondering—are we moving fast enough to build global consensus around these frameworks? In a field evolving this rapidly, what’s the path for researchers who want to do good without doing harm?
Thank you! These are great questions. It boils down to the values and priorities that drive your development. Urgent care requires speed. The companies that value and understand this will naturally build bridges and keep pace. The ones that can’t, won’t.
This was a gripping, high-stakes read—thank you, Chara, for laying out the ethical tightrope of AI security research so vividly. The “transparency trap” isn’t just a theoretical dilemma anymore; it’s already shaping how we build, trust, and regulate AI systems. The RAS-Eval case especially exposes how academic intent can collide with real-world exploitation.
I appreciate the proposed solutions around staged disclosures and defensive-first publication standards. But I can’t help wondering—are we moving fast enough to build global consensus around these frameworks? In a field evolving this rapidly, what’s the path for researchers who want to do good without doing harm?
Thank you! These are great questions. It boils down to the values and priorities that drive your development. Urgent care requires speed. The companies that value and understand this will naturally build bridges and keep pace. The ones that can’t, won’t.
Exactly that! That's why I hope that the majority of alignment research happens behind closed doors, in secrecy from the public (and AI!) eye.
Thank you Jan! There’s got to be a balance.