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A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work
Conference proceeding   Open access

A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work

Xiangzhe Yuan, Jiajun Wang, Qian Wan and Siying Hu
FAccT '25 - Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, pp.1341-1359
FAccT '25: The 2025 ACM Conference on Fairness, Accountability, and Transparency (Athens, Greece, 06/23/2025–06/26/2025)
06/23/2025
DOI: 10.1145/3715275.3732090
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1145/3715275.3732090View
Published (Version of record) Open Access

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 professional fulfillment in these occupations. However, participants also highlighted ongoing challenges, such as limited contextual details generated by the LLM and the unintentional reinforcement of existing stereotypes. Overall, our findings underscore that an LLM-based perspective-taking IF framework offers a promising and scalable strategy for mitigating stigma and promoting social equity in marginalized professions.
 AI for Social Good Dirty Work Empathy Simulation Fairness Interactive Fiction (IF) Large Language Models (LLMs) Occupational Bias Perspective-Taking Stigma Reduction UIOWA OA Agreement

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