ChatGPT versus humans in judging discriminatory scenarios: experimental evidence from a Japanese context

Abstract

Detecting and addressing discrimination is one of the most crucial ways for ethnic and sexual minorities and women to fully integrate into the society. However, humans often fail to correctly judge what constitutes discrimination. The recent rapid development of artificial intelligence (AI) based on large language models (LLMs) has shown promise in assisting with this task, although its performance is understudied. This study investigates how humans and LLM-based AI, such as OpenAI’s ChatGPT, detect the concept of ‘discrimination’ and how their representation differs. Specifically, surveys were conducted asking humans (Japanese respondents) and ChatGPT (GPT-4) to evaluate the degree of discrimination in hypothetical unequal treatment scenarios presented to them. The scenarios varied in terms of targets, including their ethnicity, gender, and sexuality, and types of discrimination, such as those based on tastes, stereotypes, and statistics. The results show that ChatGPT generally classifies the scenarios as more discriminatory than humans do. However, ChatGPT also shares a tendency with humans to be more tolerant of unequal treatment based on ethnicity and gender compared to sexuality, and it is less likely to detect statistical discrimination than taste- or stereotype-based discrimination. Although LLM-based AI presents a potential tool for addressing discrimination and can offer temporary solutions, it may not fully capture all types of discrimination.

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