Exploring the Whole Rashomon Set of Sparse Decision Trees
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Non-epistemic values, such as fairness or interpretability concerns, can guide model selection in such situations.
“Indeed, non-epistemic values(Longino 2025), such as fairness or interpretability concerns, can guide model selection in such situations, an idea now widely discussed in the model multiplicity literature(Black, Raghavan, and Barocas 2022;Xin et al. 2022;Rudin et al. 2024;Meyer et al. 2025;Höltgen and Oliver 2025).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Xin et al. 2022
Cited by
Sorted by citations
The University of North Carolina at Charlotte, Zhou, L., Rudin, C., Duke University, Gombolay, M., Georgia Institute of Technology, Spohrer, J., International Society of Service Innovation Professionals, Zhou, M., Juji, et al. (2023) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00085 · Cited by 20 · see citing works
Self-citationExplainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
- Authors
- Xin, R., Zhong, C., Chen, Z., Takagi, T., Seltzer, M., & Rudin, C.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 14071-14084
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133195854
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 9 · 2024: 2 · 2023: 1
- FWCI
- 0.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- Rashomon effect, decision tree ensembles, ensemble diversity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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