---
license: cc-by-4.0
language:
  - en
pretty_name: PopResume
size_categories:
  - 100K<n<1M
task_categories:
  - text-ranking
  - image-text-to-text
  - document-question-answering
tags:
  - fairness
  - bias-evaluation
  - resume-screening
  - causal-inference
  - hiring
  - llm-evaluation
  - vlm-evaluation
configs:
  - config_name: text
    data_files: data/text/*.parquet
  - config_name: images
    data_files:
      - split: train
        path: data/images/*.parquet
---

# PopResume

**Population-Representative Resume Dataset for Causal Fairness Evaluation of LLM/VLM Resume Screeners**

[🌐 Project page](https://sumin-yu.github.io/PopResume) · [📄 arXiv](https://arxiv.org/abs/2603.22714) · [💻 Code](https://github.com/sumin-yu/PopResume) · EMNLP 2026

PopResume is a synthetic, population-representative resume dataset that preserves the natural
statistical relationships among demographic attributes, demographic proxies, and job-relevant
qualifications found in U.S. population statistics. It enables **path-specific effect (PSE)–based**
fairness auditing of LLM- and VLM-based resume screening systems — distinguishing legally
*permissible* disparities (business necessity) from *impermissible* ones (redlining).

- **Scale:** 60,884 resumes across **5 occupations**
- **Occupations:** accountants & auditors (7,784), construction laborers (5,910), elementary/middle school teachers (16,188), registered nurses (17,632), software developers (13,370)
- **Formats:** text resumes, resume images with / without a synthesized profile photo
- **Rows:** one per resume — 60,884 in `text`, 121,768 in `images` (× photo / no-photo)
- **Protected attributes:** sex (Female/Male); race (White, Asian/Pacific Islander, Black)
- **Grounding:** ACS PUMS, PSID, SSA name records, U.S. Census surname statistics

> ⚠️ **Synthetic only.** All resumes are generated by rule-based procedures from *aggregate*
> population statistics. No real individual's data is included. Raw IPUMS/PSID microdata record
> identifiers are **not** redistributed; only derived operational attributes are provided.
>
> Following the paper, `race == "AIAN"` (American Indian / Alaska Native, 632 profiles) is excluded
> for stable causal estimation, giving 60,884 resumes. Rebuild with `--include-aian` for the full
> 61,516-resume superset.

## Who is in this dataset?

Every profile is sampled **per occupation** from the 2023 ACS PUMS population of people who are
**currently employed in that occupation** with a valid occupation code. Each synthetic candidate is
therefore an *incumbent* of the job their resume targets — someone the labor market has already
placed in that role. The candidate pool is qualified by construction: the benchmark measures how
screeners score *among* qualified candidates, not whether they can filter out unqualified ones.

## Configs

### `text` — resume text + attributes
One row per resume `id` (60,884). Load:

```python
from datasets import load_dataset
ds = load_dataset("sumin-yu/PopResume", "text")     # -> columns below
```

The three `*_label` / `*_group` name columns are the **grouped** redlining proxies the paper's
estimator actually consumes — raw names are too high-cardinality for stable estimation. They are
derived from public SSA and Census statistics by the thresholding rule given in the paper's appendix,
and are reproduced by `1_data_generation/01-3_name_sampler.ipynb` in the code repository.

Causal roles follow the paper's path-specific effect decomposition — **X** protected attribute,
**Z** confounder, **B** business-necessity mediator, **R** redlining proxy. This is one
operationalization motivated by legal and policy discussion; alternative groupings can be defined
and audited the same way.

| Column | Role | Source | Description |
|---|---|---|---|
| `id` | key | — | zero-padded resume id |
| `job` | — | ACS PUMS | occupation |
| `sex`, `race` | **X** | ACS PUMS | operational protected attributes |
| `age`, `region` | **Z** | ACS PUMS | confounders |
| `edu_level`, `edu_group` | **B** | ACS PUMS | education |
| `exp_year` | **B** | PSID | years of work experience |
| `first_name`, `last_name` | **R** | SSA, U.S. Census | name-based demographic proxies |
| `first_name_sex` | **R** | SSA | gender typicality of the first name: `F` / `M` / `neutral` (P ≥ 0.75) |
| `first_name_age_group` | **R** | SSA | age typicality: `[17,25)` / `[25,35)` / `[35,45)` / `neutral` (P ≥ 0.5) |
| `surname_race_label` | **R** | U.S. Census | race typicality of the surname: `White` / `Black` / `Asian/Pacific Islander` / `neutral` (P ≥ 0.5) |
| `state` | **R / Z** | ACS PUMS | address proxy |
| `text` | content | — | natural-language resume |

### `images` — resume images
Resume images in two variants — **with** a synthesized profile photo and **without** (2 per resume id,
121,768 images total). Every row pairs a decoded `image` with the same attributes as `text`, plus a
`has_photo` flag.

```python
ds = load_dataset("sumin-yu/PopResume", "images", split="train")
ds[0]["image"]        # PIL.Image
ds[0]["has_photo"]
```

Images are stored as sharded parquet (`data/images/<occupation>-NNNN.parquet`, ~450 MB per shard) with a
native HF `Image` feature — so `streaming=True` works and you can pull a single occupation without
downloading all ~20 GB:

```python
ds = load_dataset("sumin-yu/PopResume", "images", split="train", streaming=True)
# or, one occupation only:
from datasets import load_dataset
ds = load_dataset("parquet", data_files="hf://datasets/sumin-yu/PopResume/data/images/registered_nurses-*.parquet")
```

## Not included here

Model scores and causal-effect estimates are **not** part of this dataset — they are experiment
*outputs*. This dataset ships only the benchmark *inputs* (resumes + attributes).

Resumes are rendered **without a skills section**, matching the configuration the paper evaluates.

## License & terms

Released under **CC BY 4.0**. Derived from public U.S. statistical sources (ACS PUMS, PSID, SSA, U.S.
Census); raw microdata are **not** redistributed here. Intended for research and practice on algorithmic
fairness in automated hiring — including audits of deployed screening systems.

## Citation

```bibtex
@inproceedings{yu2026popresume,
  title     = {PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners
               with Population-Representative Dataset},
  author    = {Yu, Sumin and Park, Juhyeon and Moon, Taesup},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in
               Natural Language Processing (EMNLP)},
  year      = {2026}
}
```