| --- |
| 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} |
| } |
| ``` |
|
|
