A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work
Xiangzhe Yuan, Jiajun Wang, Qian Wan, Siying Hu

TL;DR
This study introduces an LLM-powered interactive fiction framework that enhances perspective-taking and empathy towards stigmatized 'dirty work' occupations, aiming to reduce social stigma and promote occupational equity.
Contribution
It presents a novel LLM-based interactive fiction approach to foster empathy and understanding for stigmatized roles, demonstrating its effectiveness through empirical evaluation.
Findings
Increased understanding and empathy for 'dirty work' occupations.
High immersion and emotional resonance reported by participants.
Identified challenges include limited context and stereotype reinforcement.
Abstract
Occupations referred to as "dirty work" often face entrenched social stigma, which adversely affects the mental health of workers in these fields and impedes occupational equity. In this study, we propose a novel Interactive Fiction (IF) framework powered by Large Language Models (LLMs) to encourage perspective-taking and reduce biases against these stigmatized yet essential roles. Through an experiment with participants (n = 100) across four such occupations, we observed a significant increase in participants' understanding of these occupations, as well as a high level of empathy and a strong sense of connection to individuals in these roles. Additionally, qualitative interviews with participants (n = 15) revealed that the LLM-based perspective-taking IF enhanced immersion, deepened emotional resonance and empathy toward "dirty work," and allowed participants to experience a sense of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
