# A Matter of Perspective(s): Contrasting Human and LLM Argumentation in Subjective Decision-Making on Subtle Sexism

arXiv: https://arxiv.org/abs/2502.14052 · HTML: https://mlsanity.com/paper/2502.14052/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2025.

## Built on by (most cited first, 9 of 9)

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- [Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis](https://mlsanity.com/paper/2506.12189/) (2025)
- [From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?](https://mlsanity.com/paper/2604.17968/) (2026)
- [Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning](https://mlsanity.com/paper/2512.11276/) (2025)
- [Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Framework](https://mlsanity.com/paper/2601.09342/) (2026)
- [Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders](https://mlsanity.com/paper/2607.10235/) (2026)

## Abstract (arXiv)

In subjective decision-making, where decisions are based on contextual interpretation, Large Language Models (LLMs) can be integrated to present users with additional rationales to consider. The diversity of these rationales is mediated by the ability to consider the perspectives of different social actors. However, it remains unclear whether and how models differ in the distribution of perspectives they provide. We compare the perspectives taken by humans and different LLMs when assessing subtle sexism scenarios. We show that these perspectives can be classified within a finite set (perpetrator, victim, decision-maker), consistently present in argumentations produced by humans and LLMs, but in different distributions and combinations, demonstrating differences and similarities with human responses, and between models. We argue for the need to systematically evaluate LLMs' perspective-taking to identify the most suitable models for a given decision-making task. We discuss the implications for model evaluation.