Paper 2022/360 Original: eprint.iacr.org/2022/360 ↗
Privacy-Preserving Contrastive Explanations with Local Foil Trees
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.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2022-03-30: last updated
- 2022-03-18: published
- Short URL
- https://ia.cr/2022/360
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2022/360,
author = {Thijs Veugen and Bart Kamphorst and Michiel Marcus},
title = {Privacy-Preserving Contrastive Explanations with Local Foil Trees},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/360},
year = {2022},
url = {https://eprint.iacr.org/2022/360}
}