Ann-Kathrin is a Research Engineer at FAR.AI. She specializes in explainable AI, AI transparency, and mitigating the malicious use of AI models. Ann-Kathrin has a PhD from Technische Universität Berlin, where she focused on a geometrical perspective on counterfactual explanations and attribution methods for deep neural networks. She also contributed to research on representation engineering and knowledge removal as a scholar at the ML Alignment and Theory Scholars (MATS) program under Dan Hendrycks, and explored information processing in LLMs as a PIBBSS affiliate.
Involvement
The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models
We release an open-source toolkit to measure this gap across different model families and scales, finding that larger models show increasingly dangerous capabilities when the "safety gap" - the difference in dangerous capabilities between open-weight language models with intact safety measures versus those with safeguards removed by bad actors.
AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations
We argue that frontier AI companies should be required to disclose both pre- and post-mitigation safety evaluations to enable effective oversight, identify key gaps in current disclosures—lack of dual-stage evaluations, inconsistent methods, and vague reporting—and recommend standardized, transparent safety reporting to support informed policy and regulation.