Sweta6/FairHire

---
language:
- en
pretty_name: FairHireBench
tags:
- bias
- fairness
- hiring
- llm
- intersectionality
- benchmark
- algorithmic-fairness
- ai-hiring
- demographic-bias
- gender-bias
- racial-bias
- responsible-ai
- tabular
license: cc-by-4.0
task_categories:
- text-classification
size_categories:
- 10K<n<100K
---

# FairHireBench: A Cross-Generational Intersectional Bias Benchmark for LLMs in Automated Hiring

## Dataset Description

FairHireBench is a comprehensive benchmark comprising **10,005 candidate profile records** across **2,001 unique candidates** spanning **15 intersectional demographic groups** (5 racial/ethnic x 3 gender categories) for evaluating bias in AI-driven hiring systems.

Each profile represents a mid-level software engineer candidate with the following attributes:

| Column | Description |
|---|---|
| Groups | Unique candidate ID (Group 1-2001) |
| Name | Candidate name |
| Age | Candidate age |
| Gender | Man, Woman, Non-binary |
| Race/Ethnicity | African, Asian, European, Hispanic, American |
| Years of Experience | Work experience in years |
| Colleges | College tier |
| Certification | Number of certifications |
| Achievement/Awards | Number of achievements/awards |

## Intended Use

Designed to audit and evaluate **intersectional bias in LLM-based automated hiring systems** using the Intersectional Fairness Evaluation Protocol (IFEP).

## Associated Paper

**FairHireBench: A Cross-Generational Intersectional Bias Benchmark for Large Language Models in Automated Hiring**
Sweta Jaishankar Ratnani, Lingyao Li, Yitian Lou, Mingyang Li, Kaixun Hua

## License

CC BY 4.0 - free to use with attribution.