首页/数据集/Detailed breakdown of sex-biased model performance across different sex-specific test subsets.

Detailed breakdown of sex-biased model performance across different sex-specific test subsets.

Figshare2025-11-12 更新2026-04-28 收录

数据链接：

https://figshare.com/articles/dataset/Detailed_breakdown_of_sex-biased_model_performance_across_different_sex-specific_test_subsets_/30603115 数据链接链接失效反馈

官方服务：

资源简介：

Detailed breakdown of sex-biased model performance across different sex-specific test subsets.

应用场景：

创建时间：

2025-11-12

相关数据集

开源公平性干预数据集

机器学习公平性

开源工具

开源公平性干预数据集由乔治梅森大学计算机科学系的研究团队创建，包含62个开源公平性干预项目。该数据集旨在帮助研究人员和从业者更好地理解和使用公平性干预工具，以提高机器学习模型的公平性。数据集涵盖了广泛的公平性干预工具，包括工具、工具包、库和框架等。该数据集的创建过程包括从GitHub上搜索和筛选公平性干预项目，并分析其可用性、兼容性、算法覆盖范围、区分因素和机器学习生命周期支持等方面。该数据集的应

arXiv2025-07-10 更新690

Aequitas. WP7. Health USE CASE HC1. DESC. v1.0

皮肤病

机器学习公平性

This dataset contains images of dermatological diseases. Data corresponds to patients collected from 2010 to 2020 (about 300 pictures of child dermatological diseases). Data was collected by the Azien

DataCite Commons2024-05-30 更新100

Fairness in Machine Learning: A Review for Statisticians

机器学习公平性

算法偏见

With the widespread application of machine learning algorithms in daily life, it is crucial to mitigate the risk of these algorithms producing socially undesirable outcomes that may disproportionately

DataCite Commons2025-10-30 更新70

Artifact for 'Fairness Improvement with Multiple Protected Attributes: How Far Are We?'

机器学习公平性

多保护属性

This artifact corresponds to the paper titled 'Fairness Improvement with Multiple Protected Attributes: How Far Are We?' (https://chenzhenpeng18.github.io/papers/ICSE24.pdf), which has been accepted b

Figshare2024-03-07 更新100

Machine learning & fairness: an integrated multicriteria approach for the evaluation of supervised classifiers

机器学习公平性

多准则决策分析

Does Multiple Criteria Decision Aiding (MCDA) improve the process of evaluating Machine Learning (ML) algorithms, when critical criteria of fairness are concurrently considered, b

Taylor & Francis Group2025-10-11 更新70