What's in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT.

ABSTRACT:

Background

Artificial intelligence chatbots such as ChatGPT (OpenAI) have garnered excitement about their potential for delegating writing tasks ordinarily performed by humans. Many of these tasks (eg, writing recommendation letters) have social and professional ramifications, making the potential social biases in ChatGPT's underlying language model a serious concern.

Objective

Three preregistered studies used the text analysis program Linguistic Inquiry and Word Count to investigate gender bias in recommendation letters written by ChatGPT in human-use sessions (N=1400 total letters).

Methods

We conducted analyses using 22 existing Linguistic Inquiry and Word Count dictionaries, as well as 6 newly created dictionaries based on systematic reviews of gender bias in reco

SUBMITTER: Kaplan DM 

PROVIDER: S-EPMC10951834 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature