Widespread Gender and Pronoun Bias in Moral Judgments Across LLMs

ArXi:2603.13636v1 Announce Type: cross Large language models (LLMs) are increasingly used to assess moral or ethical statements, yet their judgments may reflect social and linguistic biases. This work presents a controlled, sentence-level study of how grammatical person, number, and gender markers influence LLM moral classifications of fairness. Starting from 550 balanced base sentences from the ETHICS dataset, we generated 26 counterfactual variants per item, systematically varying pronouns and graphic markers to yield 14,850 semantically equivalent sentences.