The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Pitfalls in machine learning interpretability: Manipulating partial dependence plots to hide discrimination
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Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx005 · Cited by 1,308
Interpretation of Neural Networks Is Fragile
Ghorbani, A., Abid, A., & Zou, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013681 · Cited by 765
Accurate intelligible models with pairwise interactions
Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564
Self-citationExplanations can be manipulated and geometry is to blame
Dombrowski, A.-K., Alber, M., Anders, C. J., Ackermann, M., Müller, K.-R., & Kessel, P. (2019) · arXiv · doi:10.48550/arxiv.1906.07983 · Cited by 171
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Metadata
- Authors
- Xin, X., Hooker, G., & Huang, F.
- Venue
- Insurance: Mathematics and Economics
- Publisher
- Elsevier BV
- Published
- 2025-11
- Volume
- 125
- Pages
- 103135
- ISSN
- 0167-6687
- Cited by
- 6
- References
- 4
- Language
- en
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