The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
The hidden assumptions behind counterfactual explanations and principal reasons
Open accessAbstract
Counterfactual explanations are gaining prominence within technical, legal, and business circles as a way to explain the decisions of a machine learning model. These explanations share a trait with the long-established "principal reason" explanations required by U.S. credit laws: they both explain a decision by highlighting a set of features deemed most relevant---and withholding others.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Tyler, T. R. (2006) · doi:10.1515/9781400828609 · Cited by 5,156
Privacy in Context: Technology, Policy, and the Integrity of Social Life
Nissenbaum, H. (2009) · doi:10.1515/9780804772891 · Cited by 2,129 · cited 1×
“integrity [30].”
Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631
Self-citationSolove, D. J. (2006) · University of Pennsylvania Law Review · doi:10.2307/40041279 · Cited by 1,388
Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx005 · Cited by 1,308
Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233
Intelligible models for classification and regression
Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547
Edwards, L., & Veale, M. (2017) · doi:10.31228/osf.io/97upg · Cited by 524
Explaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264
Research works printed but not linked: 28
- Balkin Jack M, 2015, Information fiduciaries and the first amendment, UC Davis Law Review, 49, 1183
- Bambauer Jane, 2018, The algorithm game, Notre Dame L. Rev., 94, 1
- Brkan Maja, 2019, International journal of law and information technology 27, 2
- Citron Danielle Keats, 2014, The scored society: Due process for automated predictions, Washington Law Review, 89, 1
- Cohen Julie E, Configuring the networked self: Law, code, and the play of everyday practice
- Cohen Julie E, 2012, What privacy is for, Harvard Law Review, 126, 1904
- Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Paishun Ting Karthikeyan Shanmugam and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems. 592--603. Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Paishun Ting Karthikeyan Shanmugam and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems. 592--603.
- Eubanks Virginia, Automating inequality: How high-tech tools profile, police, and punish the poor
- Grath Rory Mc, 2018, Paul Sweeney, Farbod Kamiab, Zhao Shen, and Freddy Lecue.
- Hendricks Lisa Anne, 2018, Generating counterfactual explanations with natural language. arXiv preprint arXiv:1806.09809 · arXiv
- 10.1007/s11623-006-0140-3 · DOI
- Karimi Amir-Hossein, 2019, Model-Agnostic Counterfactual Explanations for Consequential Decisions. arXiv preprint arXiv:1905.11190 · arXiv
- Khan Lina, 2019, A Skeptical View of Information Fiduciaries. Harvard Law Review 133
- 10.1145/3328526.3329584 · DOI
- Lipton Zachary, 2017, NYU Algorithms and Explanations Conference.
- Liu Lydia T., 2018, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan
- 10.1093/idpl/ipx019 · DOI · Paper page
- Mendoza Isak, EU Internet Law
- Packin Nizan Geslevich, 2016, On social credit and the right to be unnetworked. Columbia Business Law Review
- Reiman Jeffrey H, 1976, intimacy, and personhood. Philosophy & Public Affairs
- 10.1145/2939672.2939778 · DOI · Paper page
- 10.1145/3287560.3287569 · DOI · Paper page
- Selbst Andrew D., 2018, The intuitive appeal of explainable machines, Fordham Law Review, 87, 1085
- 10.1093/idpl/ipx022 · DOI · Paper page
- Senate Report No. 94-589 1976. Senate Report No. 94-589 1976. · DOI
- Tramèr Florian, 25th {USENIX} Security Symposium ({USENIX} Security 16). 601--618.
- 10.1145/3287560.3287566 · DOI · Paper page
- Wachter Sandra, 2017, Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GPDR, Journal of Law & Technology, 31, 841
Cited by
Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022) · Organization Science · doi:10.1287/orsc.2021.1549 · Cited by 632
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023) · ACM Computing Surveys · doi:10.1145/3527848 · Cited by 301
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302
Algorithmic recourse: From counterfactual explanations to interventions
Karimi, A.-H., Schölkopf, B., & Valera, I. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445899 · Cited by 274
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Metadata
- Authors
- Barocas, S., Selbst, A. D., & Raghavan, M.
- Venue
- Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2020-01-27
- Pages
- 80-89
- OpenAlex
- W2995528912
- Cited by
- 216
- References
- 14
- FWCI
- 15.52
- Type
- conference paper
- Language
- en
- Open access
- gold
- Funders
- National Science Foundation (IIS-1633400)
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The hidden assumptions behind counterfactual explanations and principal reasons. https://app.cochat.ai/papers/10.1145/3351095.3372830
- The paper
- Barocas, S., Selbst, A. D., & Raghavan, M. (2020). The hidden assumptions behind counterfactual explanations and principal reasons. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3351095.3372830
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
