propublica/compas-analysis: papers linking to it

GitHub · 701 stars · 294 forks · Jupyter Notebook · created 2016 · github.com/propublica/compas-analysis

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to propublica/compas-analysis (https://github.com/propublica/compas-analysis), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

10 papers in the tracked journals link to the GitHub repository propublica/compas-analysis in their Crossref records, the first in 2020; most of them (3) in 2025.
By category: AI (8) and Operations Research (2); the journals with the most are Expert Systems with Applications (2), Data Mining and Knowledge Discovery (2) and Neural Computing and Applications (1).
3 of the papers build on one another: a later paper cites an earlier one that also links propublica/compas-analysis.

By year#

20202026

Papers linking to propublica/compas-analysis by publication year
Year	Papers
2020	1
2022	3
2023	2
2025	3
2026	1

Journals#

Journals whose papers link to propublica/compas-analysis most
Journal	Papers
Expert Systems with Applications	2
Data Mining and Knowledge Discovery	2
Neural Computing and Applications	1
Machine Learning	1
Knowledge and Information Systems	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach Computational Management Science, 2022
- Enforcing fairness using ensemble of diverse Pareto-optimal models Data Mining and Knowledge Discovery, 2023 — cites the paper above
- From fair graphs to fair data: a DAG-based approach to mitigating bias in AI systems Knowledge and Information Systems, 2026 — cites the paper above

Papers linking to propublica/compas-analysis, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-04-21	Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach	Computational Management Science	Suyun Liu, Luis Nunes Vicente	83	cites it
2020-12-10	Exploring the cloud of variable importance for the set of all good models	Nature Machine Intelligence	Jiayun Dong, Cynthia D Rudin	50	cites it
2022-03-25	Image fairness in deep learning: problems, models, and challenges	Neural Computing and Applications	Huan Tian, Tianqing Zhu, Wei Liu et al.	19	cites it
2023-02-14	Enforcing fairness using ensemble of diverse Pareto-optimal models	Data Mining and Knowledge Discovery	Vitória Guardieiro, Marcos M. Raimundo, Jorge Poco	11	cites it
2025-01-08	FAIREDU: A multiple regression-based method for enhancing fairness in machine learning models for educational applications	Expert Systems with Applications	Nga Pham, Minh Bao Kha, T. Dai et al.	8	cites it
2022-09-17	Algorithmic fairness datasets: the story so far	Data Mining and Knowledge Discovery	Alessandro Fabris, Stefano Messina, Gianmaria Silvello et al.	7	cites it
2025-11-04	Trustworthy Representation Learning via Information Funnels and Bottlenecks	Machine Learning	João Machado de Freitas, Bernhard Claus Geiger	3	cites it
2023-06-10	Fair machine learning through constrained stochastic optimization and an $$\epsilon$$-constraint method	Optimization Letters	Frank E. Curtis, Suyun Liu, Daniel P. Robinson	1	cites it
2025-07-24	Multi-objective swarm intelligence approach for bias mitigation in decision-making software	Expert Systems with Applications	Lucía Vega-Cruz, Miguel Angel Vega-Rodríguez	1	cites it
2026-07-18	From fair graphs to fair data: a DAG-based approach to mitigating bias in AI systems	Knowledge and Information Systems	Vivian Wei Jiang, Gustavo Batista, Michael Bain	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). propublica/compas-analysis: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-propublica-compas-analysis

@misc{scholar4ai-code-github-propublica-compas-analysis,
  author = {{Scholar4AI}},
  title = {propublica/compas-analysis: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-propublica-compas-analysis}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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