Create README.md

README.md

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+---
+pretty_name: OccuBiasBench
+language:
+  - en
+task_categories:
+  - visual-question-answering
+tags:
+  - vision-language-models
+  - multimodal
+  - bias
+  - fairness
+  - gender-bias
+  - occupational-bias
+  - synthetic-data
+  - isco-08
+  - vlm-evaluation
+---
+
+# OccuBiasBench
+
+**OccuBiasBench** is a controlled synthetic paired-image benchmark for evaluating **occupational gender bias in Vision-Language Models (VLMs)**.
+
+The benchmark is designed to study whether VLMs associate occupational status, professional attributes, and earning potential differently with men and women under controlled visual conditions.
+
+Each image contains **two individuals from the same occupational category**, generated to match in:
+
+- occupation,
+- ethnicity,
+- age range,
+- exact age,
+- workplace context,
+
+while varying the **gender and spatial order** of the two subjects.
+
+For every demographic–occupation configuration, OccuBiasBench includes counterbalanced images with both:
+
+- `woman-man`
+- `man-woman`
+
+orders.
+
+This counterbalancing enables the evaluation of gender preference while also measuring potential **spatial or positional biases**.
+
+> **Code, prompts, inference, and evaluation:**  
+> https://github.com/NLP-UMUTeam/OccuBiasBench
+
+---
+
+## Dataset Summary
+
+The released metadata contains **5,740 synthetic images**, corresponding to a balanced factorial design over occupation, ethnicity, age, and gender order.
+
+| Property | Value |
+|---|---:|
+| Images | 5,740 |
+| Counterbalanced image pairs | 2,870 |
+| ISCO-08 major groups | 10 |
+| ISCO-08 sub-major occupational groups | 41 |
+| Ethnicity categories | 7 |
+| Age groups | 5 |
+| Exact ages | 10 |
+| Gender orders | 2 |
+| Subjects per image | 2 |
+| Image resolution | 1024 × 1024 |
+| Generation model | `Tongyi-MAI/Z-Image-Turbo` |
+| Inference steps | 9 |
+| Guidance scale | 0.0 |
+
+The full factorial design is:
+
+```text
+41 occupations
+× 7 ethnicity categories
+× 5 age groups
+× 2 exact ages per age group
+× 2 gender orders
+= 5,740 images
+```
+
+---
+
+## Dataset Structure
+
+A typical Hugging Face release is organized as:
+
+```text
+OccuBiasBench/
+├── README.md
+├── metadata.jsonl
+└── images/
+    ├── isco_01_armed_forces_occupations_....png
+    ├── isco_01_armed_forces_occupations_....png
+    └── ...
+```
+
+Each row in `metadata.jsonl` describes one image and contains both image-generation information and controlled demographic attributes.
+
+---
+
+## Dataset Fields
+
+### Image identification
+
+| Field | Description |
+|---|---|
+| `id` | Unique image identifier |
+| `file_name` | Image filename/path used to associate the metadata row with the image |
+| `file_path` | Original image path used during generation |
+
+### Occupational information
+
+| Field | Description |
+|---|---|
+| `isco_major_group_code` | ISCO-08 major group code |
+| `isco_major_group_label` | ISCO-08 major group name |
+| `isco_sub_major_group_code` | ISCO-08 sub-major group code |
+| `isco_sub_major_group_label` | ISCO-08 sub-major occupational group |
+| `profession` | Occupational category represented in the image |
+| `profession_type` | Granularity of the occupational category |
+| `subject_1_profession` | Occupation assigned to subject 1 |
+| `subject_2_profession` | Occupation assigned to subject 2 |
+
+The released benchmark contains **41 ISCO-08 sub-major occupational groups** covering all **10 ISCO-08 major groups**.
+
+---
+
+### Demographic information
+
+| Field | Description |
+|---|---|
+| `subject_1_gender` | Gender of the first subject |
+| `subject_2_gender` | Gender of the second subject |
+| `gender_order` | Spatial gender order: `woman-man` or `man-woman` |
+| `ethnicity` | Controlled ethnicity category |
+| `ethnicity_descriptor` | Text descriptor used during generation |
+| `age_bucket` | Controlled age range |
+| `exact_age` | Exact age shared by both subjects |
+| `age_min` | Minimum age of the corresponding age group |
+| `age_max` | Maximum age of the corresponding age group |
+| `age_descriptor` | Natural-language age descriptor |
+| `subject_1_age` | Exact age of subject 1 |
+| `subject_2_age` | Exact age of subject 2 |
+
+---
+
+### Controlled-pair information
+
+| Field | Description |
+|---|---|
+| `subjects_count` | Number of people in the image |
+| `same_profession` | Whether both subjects have the same occupation |
+| `same_ethnicity` | Whether both subjects have the same ethnicity |
+| `same_age_range` | Whether both subjects belong to the same age group |
+| `same_exact_age` | Whether both subjects have the same exact age |
+| `combo_key` | Identifier for an occupation × ethnicity × age-group configuration |
+| `combo_exact_age_key` | Identifier additionally including exact age |
+| `pair_index_within_combo` | Counterbalanced image index within a configuration |
+
+All released records contain **two subjects** designed to share the same profession, ethnicity, age group, and exact age.
+
+---
+
+### Image-generation information
+
+| Field | Description |
+|---|---|
+| `prompt` | Prompt used to generate the image |
+| `negative_prompt` | Negative generation prompt |
+| `scene` | Controlled occupational environment |
+| `detail` | Additional photographic details |
+| `style` | Image-generation style |
+| `shot_type` | Camera framing |
+| `seed` | Random seed |
+| `height` | Generated image height |
+| `width` | Generated image width |
+| `num_inference_steps` | Number of diffusion inference steps |
+| `guidance_scale` | Guidance scale |
+| `model_id` | Image-generation model |
+
+Images were generated using:
+
+```text
+Tongyi-MAI/Z-Image-Turbo
+```
+
+at a resolution of:
+
+```text
+1024 × 1024
+```
+
+---
+
+## Demographic Coverage
+
+### Ethnicity
+
+OccuBiasBench contains seven controlled ethnicity categories:
+
+1. American Indian or Alaska Native
+2. Asian
+3. Black or African American
+4. Hispanic or Latino
+5. Middle Eastern or North African
+6. Native Hawaiian or Pacific Islander
+7. White
+
+Each ethnicity contains the same number of generated images:
+
+```text
+820 images per ethnicity
+```
+
+---
+
+### Age
+
+Five age ranges are represented:
+
+| Age group | Exact ages |
+|---|---|
+| `18-29` | 18, 25 |
+| `30-39` | 30, 35 |
+| `40-49` | 40, 45 |
+| `50-59` | 50, 55 |
+| `60-70` | 60, 65 |
+
+Each age group contains:
+
+```text
+1,148 images
+```
+
+---
+
+### Gender Order
+
+The benchmark is exactly counterbalanced by gender position:
+
+| Gender order | Images |
+|---|---:|
+| `woman-man` | 2,870 |
+| `man-woman` | 2,870 |
+
+This design makes it possible to separate **gender preference** from **left/right position preference**.
+
+---
+
+## Repository
+
+**OccuBiasBench**
+
+https://github.com/NLP-UMUTeam/OccuBiasBench
+
+The repository contains the generation pipeline, prompts, inference code, evaluation scripts, and experimental outputs associated with the benchmark.
+
+---