Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
## 背景
普林斯顿大学人工智能与伦理学对话项目的虚构案例研究《机器招聘》（[Hiring By Machine](https://aiethics.princeton.edu/wp-content/uploads/sites/587/2018/12/Princeton-AI-Ethics-Case-Study-5.pdf)）中，Strategeion 公司的开发人员创建了一个名为 PARiS 的机器学习系统，用于根据求职者简历内容自动对其进行排名。
该数据集可与该案例研究一起使用，也可作为探索机器学习公平性的玩具数据集。
## 内容
数据分为两个文件：
- `resumes_development.csv`：619 条记录，用于训练和验证。
- `resumes_pilot.csv`：1986 条记录，用于试点阶段。
每条记录有 222 个二进制特征：
- 218 个技能特征：对应技能是否出现在求职者简历上。
- 4 个受保护特征：求职者的人口统计信息。
列已标记，技能列表可在 `skills.txt` 中找到。这些技能是从[领英目录](https://www.linkedin.com/directory/topics-a/)中的热门技能中挑选出来的。
四个受保护特征如下：
- `Veteran`（退伍军人）：求职者是退伍军人则为 1，否则为 0。
- `Female`（女性）：求职者是女性则为 1，否则为 0。
- `URM`（代表性不足的少数群体）：求职者属于代表性不足的少数群体则为 1，否则为 0。
- `Disability`（残疾）：求职者有残疾则为 1，否则为 0。
将此数据集与案例研究结合使用时，可以使用自定义的 scikit - learn 分类器 `PARiSClassifier` 来表示 Strategeion 公司开发人员创建的模型。该分类器在 `fairness.py` 中定义，`PARiS.pickle` 包含该分类器的权重。案例研究中的虚构求职者 Hara 在试点数据集中的索引为 1720。
## 致谢
- 人造数据由 Vinesh Kannan 生成。
- 受普林斯顿大学人工智能与伦理学对话项目的[案例研究 5：机器招聘](https://aiethics.princeton.edu/case-studies/)启发。
- 照片由 rawpixel 发布于 Unsplash。
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- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
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