Abstract:

Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an “interpretable” feature representation. In tabular data, feature values themselves are often considered interpretable. This paper examines the impact of data engineering choices on local feature-based explanations. We demonstrate that simple, common data engineering techniques, such as representing age with a histogram or encoding race in a specific way, can manipulate feature importance as determined by popular methods like SHAP. Notably, the sensitivity of explanations to feature representation can be exploited by adversaries to obscure issues like discrimination. While the intuition behind these results is straightforward, their systematic exploration has been lacking. Previous work has focused on adversarial attacks on feature-based explainers by biasing data or manipulating models. To the best of our knowledge, this is the first study demonstrating that explainers can be misled by standard, seemingly innocuous data engineering techniques.

Citation

Hwang, Hyunseung, Bell, Andrew, Fonseca, Joao, Pliatsika, Venetia, Stoyanovich, Julia, and Whang, Steven Euijong. 2025. “SHAP-based Explanations are Sensitive to Feature Representation.” Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency: 1588–1601. https://doi.org/10.1145/3715275.3732105.

@inproceedings{hwang2025shap,
  author = {Hwang, Hyunseung and Bell, Andrew and Fonseca, Joao and Pliatsika, Venetia and Stoyanovich, Julia and Whang, Steven Euijong},
  title = {{SHAP}-based Explanations are Sensitive to Feature Representation},
  booktitle = {Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency},
  year = {2025},
  pages = {1588–1601},
  doi = {10.1145/3715275.3732105},
  url = {https://doi.org/10.1145/3715275.3732105},
  abstract = {Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an “interpretable” feature representation. In tabular data, feature values themselves are often considered interpretable. This paper examines the impact of data engineering choices on local feature-based explanations. We demonstrate that simple, common data engineering techniques, such as representing age with a histogram or encoding race in a specific way, can manipulate feature importance as determined by popular methods like SHAP. Notably, the sensitivity of explanations to feature representation can be exploited by adversaries to obscure issues like discrimination. While the intuition behind these results is straightforward, their systematic exploration has been lacking. Previous work has focused on adversarial attacks on feature-based explainers by biasing data or manipulating models. To the best of our knowledge, this is the first study demonstrating that explainers can be misled by standard, seemingly innocuous data engineering techniques.},
  address = {New York, NY, USA},
  isbn = {9798400714825},
  keywords = {Explainable AI, SHAP, Feature Representation},
  location = {},
  numpages = {14},
  publisher = {Association for Computing Machinery},
  series = {FAccT '25}
}