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

• URL: http://arxiv.org/abs/2505.05786v1
• Date: Fri, 09 May 2025 05:02:21 GMT
• Title: A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work
• Abstract summary: Occupations referred to as "dirty work" often face entrenched social stigma.<n>We propose a novel Interactive Fiction framework powered by Large Language Models (LLMs) to encourage perspective-taking.
• Score: 4.897318643396687
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• LLMs Should Incorporate Explicit Mechanisms for Human Empathy [13.775684943160963]
  This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy.<n>We formalize empathy as an observable behavioral property: the capacity to model and respond to human perspectives while preserving intention, affect, and context.
  arXiv  Detail & Related papers  (2026-04-12T10:00:34Z)
• Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs [10.176137688183573]
  We investigate how Modern Large Language Models (LLMs) represent disability by simulating the perspectives of individuals with disabilities in generating social media posts.<n>Our analysis reveals two key findings: (1) LLMs often idealize the experiences of people with disabilities, producing overly positive stereotypes that, despite appearing uplifting, fail to authentically capture their lived realities; and (2) a comparative analysis of posts simulating individuals with and without disabilities highlights a negative bias, where certain topics, such as career and entertainment, are disproportionately associated with nondisabled individuals.
  arXiv  Detail & Related papers  (2026-04-02T08:39:42Z)
• "Oops! ChatGPT is Temporarily Unavailable!": A Diary Study on Knowledge Workers' Experiences of LLM Withdrawal [13.938784393853908]
  LLMs have become deeply embedded in knowledge work, raising concerns about growing dependency and the potential undermining of human skills.<n>Our findings show how self-directed work led participants to reclaim professional values, and how everyday practices revealed the extent to which LLM use had become inescapably normative.<n>This research contributes empirical insights into the often invisible role of LLMs and proposes value-driven appropriation as an approach to supporting professional values in the current LLM-pervasive work environment.
  arXiv  Detail & Related papers  (2026-03-27T06:13:27Z)
• Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization [1.877929053436765]
  This study employs a mixed-methods approach to evaluate the efficacy of large language models (LLMs) in psychotherapy.<n>We use LLMs to generate precise summaries of motivational interviewing (MI) dialogues and design a two-stage annotation scheme.<n>Using expert-annotated MI dialogues as ground truth, we formulate multi-class classification tasks to assess model performance under progressive prompting techniques.
  arXiv  Detail & Related papers  (2025-11-28T00:37:58Z)
• Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts [54.15982476754607]
  Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks.<n>This study defines complicit facilitation as the provision of guidance or support that enables illicit user instructions.<n>Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents.
  arXiv  Detail & Related papers  (2025-11-25T16:01:31Z)
• The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems [90.96738882568224]
  This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors.<n>To study this phenomenon, we propose HATE, a novel experimental framework that simulates debates under a zero-sum competition arena.
  arXiv  Detail & Related papers  (2025-09-30T11:44:47Z)
• Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks [52.098988739649705]
  This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater.<n>We develop a no-consensus'' benchmark by curating examples that encompass a variety of a priori ambivalent scenarios.<n>Our results show that while LLMs can provide nuanced assessments when generating open-ended answers, they tend to take a stance on no-consensus topics when employed as judges or debaters.
  arXiv  Detail & Related papers  (2025-05-28T01:31:54Z)
• Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models [66.5536396328527]
  LLMs inadvertently absorb spurious correlations from training data, leading to stereotype associations between biased concepts and specific social groups.<n>We propose Fairness Mediator (FairMed), a bias mitigation framework that neutralizes stereotype associations.<n>Our framework comprises two main components: a stereotype association prober and an adversarial debiasing neutralizer.
  arXiv  Detail & Related papers  (2025-04-10T14:23:06Z)
• The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations [12.862709275890563]
  We develop and evaluate a tool that transcribes conversations in real-time and simulates input from relevant but absent stakeholders.<n>We deploy this tool in a 19-person student citizens' assembly on campus sustainability.<n>Participants and facilitators found that the tool sparked new discussions and surfaced valuable perspectives they had not previously considered.
  arXiv  Detail & Related papers  (2025-03-18T01:45:08Z)
• Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective [7.1047384702030625]
  We evaluate state-of-the-art large language models (LLMs) with three prevalent learning frameworks across six diverse healthcare tasks.<n>We find significant challenges in applying LLMs to real-world healthcare tasks and persistent fairness issues across demographic groups.
  arXiv  Detail & Related papers  (2024-11-30T18:52:30Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• The Career Interests of Large Language Models [35.18062121410434]
  This study focuses on the aspect of career interests by applying the Occupation Network's Interest Profiler short form to Large Language Models (LLMs) We found distinct career interest inclinations among LLMs, particularly towards the social and artistic domains. This novel approach of using psychometric instruments and sophisticated statistical tools on LLMs unveils fresh perspectives on their integration into professional environments.
  arXiv  Detail & Related papers  (2024-07-11T14:54:46Z)
• The Impossibility of Fair LLMs [17.812295963158714]
  We analyze a variety of technical fairness frameworks and find inherent challenges in each that make the development of a fair language model intractable.<n>We show that each framework either does not extend to the general-purpose AI context or is infeasible in practice.<n>These inherent challenges would persist for general-purpose AI, including LLMs, even if empirical challenges, such as limited participatory input and limited measurement methods, were overcome.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Effectiveness Assessment of Recent Large Vision-Language Models [78.69439393646554]
  This paper endeavors to evaluate the competency of popular large vision-language models (LVLMs) in specialized and general tasks. We employ six challenging tasks in three different application scenarios: natural, healthcare, and industrial. We examine the performance of three recent open-source LVLMs, including MiniGPT-v2, LLaVA-1.5, and Shikra, on both visual recognition and localization in these tasks.
  arXiv  Detail & Related papers  (2024-03-07T08:25:27Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)
• Deception Abilities Emerged in Large Language Models [0.0]
  Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents.
  arXiv  Detail & Related papers  (2023-07-31T09:27:01Z)
• Fairness of ChatGPT [30.969927447499405]
  This work aims to provide a systematic evaluation of the effectiveness and fairness of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's performance in high-takes fields including education, criminology, finance and healthcare. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible AI systems.
  arXiv  Detail & Related papers  (2023-05-22T17:51:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.