---
title: "propublica/compas-analysis: papers citing it"
description: "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."
url: https://scholar4ai.com/code/github-propublica-compas-analysis
markdown_url: https://scholar4ai.com/code/github-propublica-compas-analysis.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# propublica/compas-analysis: papers linking to it

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.

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

All repositories: https://scholar4ai.com/code.md

## 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

*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](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 2 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach](https://doi.org/10.1007/s10287-022-00425-z) (Computational Management Science, 2022)
  - [Enforcing fairness using ensemble of diverse Pareto-optimal models](https://doi.org/10.1007/s10618-023-00922-y) (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](https://doi.org/10.1007/s10115-026-02817-y) (Knowledge and Information Systems, 2026) — cites the paper above

## Papers

*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](https://doi.org/10.1007/s10287-022-00425-z) | [Computational Management Science](https://scholar4ai.com/journals/computational-management-science.md) | Suyun Liu, Luis Nunes Vicente | 83 | cites it |
| 2020-12-10 | [Exploring the cloud of variable importance for the set of all good models](https://doi.org/10.1038/s42256-020-00264-0) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Jiayun Dong, Cynthia D Rudin | 50 | cites it |
| 2022-03-25 | [Image fairness in deep learning: problems, models, and challenges](https://doi.org/10.1007/s00521-022-07136-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Huan Tian, Tianqing Zhu, Wei Liu et al. | 19 | cites it |
| 2023-02-14 | [Enforcing fairness using ensemble of diverse Pareto-optimal models](https://doi.org/10.1007/s10618-023-00922-y) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 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](https://doi.org/10.1016/j.eswa.2024.126219) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Nga Pham, Minh Bao Kha, T. Dai et al. | 8 | cites it |
| 2022-09-17 | [Algorithmic fairness datasets: the story so far](https://doi.org/10.1007/s10618-022-00854-z) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Alessandro Fabris, Stefano Messina, Gianmaria Silvello et al. | 7 | cites it |
| 2025-11-04 | [Trustworthy Representation Learning via Information Funnels and Bottlenecks](https://doi.org/10.1007/s10994-025-06924-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 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](https://doi.org/10.1007/s11590-023-02024-6) | [Optimization Letters](https://scholar4ai.com/journals/optimization-letters.md) | 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](https://doi.org/10.1016/j.eswa.2025.129084) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 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](https://doi.org/10.1007/s10115-026-02817-y) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).