Welcome to the resource topic for 2022/360
Title:
Privacy-Preserving Contrastive Explanations with Local Foil Trees
Authors: Thijs Veugen, Bart Kamphorst, Michiel Marcus
Abstract:We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the quality of these explanations can be upheld whilst ensuring the privacy of both the training data, and the model itself.
ePrint: https://eprint.iacr.org/2022/360
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .