# BAD: BiAs Detection for Large Language Models in the context of candidate screening

arXiv: https://arxiv.org/abs/2305.10407 · HTML: https://mlsanity.com/paper/2305.10407/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 3 of 3)

- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)

## Built on by (most cited first, 8 of 8)

- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [Building Guardrails for Large Language Models](https://mlsanity.com/paper/2402.01822/) (2024)
- [Tell, don't show: Declarative facts influence how LLMs generalize](https://mlsanity.com/paper/2312.07779/) (2023)
- [Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanations](https://mlsanity.com/paper/2509.04515/) (2025)
- [Evaluation of Large Language Models: STEM education and Gender Stereotypes](https://mlsanity.com/paper/2406.10133/) (2024)
- [Bias Testing and Mitigation in Black Box LLMs using Metamorphic Relations](https://mlsanity.com/paper/2512.00556/) (2025)
- [Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes](https://mlsanity.com/paper/2609.22188/) (2026)

## Abstract (arXiv)

Application Tracking Systems (ATS) have allowed talent managers, recruiters, and college admissions committees to process large volumes of potential candidate applications efficiently. Traditionally, this screening process was conducted manually, creating major bottlenecks due to the quantity of applications and introducing many instances of human bias. The advent of large language models (LLMs) such as ChatGPT and the potential of adopting methods to current automated application screening raises additional bias and fairness issues that must be addressed. In this project, we wish to identify and quantify the instances of social bias in ChatGPT and other OpenAI LLMs in the context of candidate screening in order to demonstrate how the use of these models could perpetuate existing biases and inequalities in the hiring process.