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Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success

Research article (Proceedings of the Seventh ACM Conference on Learning @ Scale, 2020) · cited 23× · AI/ML
academia openalex:W3046298089
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Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success

Summary

Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success is a scholarly article[1].

Key Facts

  • Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success. Retrieved May 24, 2026, from https://4ort.xyz/entity/interpretable-models-do-not-compromise-accuracy-or-fairness-in-predicting-college-success
MLA “Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/interpretable-models-do-not-compromise-accuracy-or-fairness-in-predicting-college-success.
BibTeX @misc{4ortxyz_interpretable-models-do-not-compromise-accuracy-or-fairness-in-predicting-college-success_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success}}, year = {2026}, url = {https://4ort.xyz/entity/interpretable-models-do-not-compromise-accuracy-or-fairness-in-predicting-college-success}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success — https://4ort.xyz/entity/interpretable-models-do-not-compromise-accuracy-or-fairness-in-predicting-college-success (retrieved 2026-05-24)

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