Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)

AAAI 2022 pp. 12907-12908

doi:10.1609/AAAI.V36I11.21590 /aaai/2022/baniecki2022aaai-manipulating/

Abstract

We introduce a model-agnostic algorithm for manipulating SHapley Additive exPlanations (SHAP) with perturbation of tabular data. It is evaluated on predictive tasks from healthcare and financial domains to illustrate how crucial is the context of data distribution in interpreting machine learning models. Our method supports checking the stability of the explanations used by various stakeholders apparent in the domain of responsible AI; moreover, the result highlights the explanations' vulnerability that can be exploited by an adversary.

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Cite

Text

Baniecki and Biecek. "Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21590

Markdown

[Baniecki and Biecek. "Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/baniecki2022aaai-manipulating/) doi:10.1609/AAAI.V36I11.21590

BibTeX

@inproceedings{baniecki2022aaai-manipulating,
  title     = {{Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)}},
  author    = {Baniecki, Hubert and Biecek, Przemyslaw},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12907-12908},
  doi       = {10.1609/AAAI.V36I11.21590},
  url       = {https://mlanthology.org/aaai/2022/baniecki2022aaai-manipulating/}
}