Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager

The growing importance of large language models (LLMs) in daily life has heightened awareness and concerns about the fact that LLMs exhibit many of the same biases as their creators. In the context of hiring decisions, we quantify the degree to which LLMs perpetuate biases originating from their training data and investigate prompt engineering as a bias-mitigation technique. Our findings suggest that for a given resumé, an LLM is more likely to hire a candidate and perceive them as more qualified if the candidate is female, but still recommends lower pay relative to male candidates.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Gerszberg, Nina R.

Advisor dc:contributor.advisor

• Lo, Andrew

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156812

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156812