144 citations · 335 across the 12 of their papers we have counts for
5 papers · 1 filter
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal +54
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and che…
Representation Engineering: A Top-Down Approach to AI Transparency
Andy Zou, Long Phan, Sarah Chen +18
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights f…
Diffeomorphic Counterfactuals with Generative Models
Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller +1
Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. Mor…
Towards Robust Explanations for Deep Neural Networks
Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller +1
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible t…
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski +2
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making…