Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation

• URL: http://arxiv.org/abs/2401.16558v1
• Date: Mon, 29 Jan 2024 20:50:28 GMT
• Title: Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation
• Authors: Terrence Neumann, Sooyong Lee, Maria De-Arteaga, Sina Fazelpour, Matthew Lease
• Abstract summary: This paper examines whether a large language model (LLM) can reflect the views of various groups when assessing the harms of misinformation. We present the TopicMisinfo dataset, containing 160 fact-checked claims from diverse topics. We find that GPT 3.5-Turbo reflects empirically observed gender differences in opinion but amplifies the extent of these differences.
• Score: 8.066880413153187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pervasive spread of misinformation and disinformation poses a significant threat to society. Professional fact-checkers play a key role in addressing this threat, but the vast scale of the problem forces them to prioritize their limited resources. This prioritization may consider a range of factors, such as varying risks of harm posed to specific groups of people. In this work, we investigate potential implications of using a large language model (LLM) to facilitate such prioritization. Because fact-checking impacts a wide range of diverse segments of society, it is important that diverse views are represented in the claim prioritization process. This paper examines whether a LLM can reflect the views of various groups when assessing the harms of misinformation, focusing on gender as a primary variable. We pose two central questions: (1) To what extent do prompts with explicit gender references reflect gender differences in opinion in the United States on topics of social relevance? and (2) To what extent do gender-neutral prompts align with gendered viewpoints on those topics? To analyze these questions, we present the TopicMisinfo dataset, containing 160 fact-checked claims from diverse topics, supplemented by nearly 1600 human annotations with subjective perceptions and annotator demographics. Analyzing responses to gender-specific and neutral prompts, we find that GPT 3.5-Turbo reflects empirically observed gender differences in opinion but amplifies the extent of these differences. These findings illuminate AI's complex role in moderating online communication, with implications for fact-checkers, algorithm designers, and the use of crowd-workers as annotators. We also release the TopicMisinfo dataset to support continuing research in the community.

Related papers

• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [58.130894823145205]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning. We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval [56.66761232081188]
  We present a novel dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.
  arXiv  Detail & Related papers  (2024-07-29T03:14:57Z)
• GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
  This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models. Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs. Our findings reveal widespread gender biases in existing LVLMs.
  arXiv  Detail & Related papers  (2024-06-30T05:55:15Z)
• Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks [5.123567809055078]
  Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks.
  arXiv  Detail & Related papers  (2024-05-27T06:20:58Z)
• Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes [12.704072523930444]
  This study investigates eleven strategies to automatically counter-act and challenge gender stereotypes in online communications. We present AI-generated gender-based counter-stereotypes to study participants and ask them to assess their offensiveness, plausibility, and potential effectiveness.
  arXiv  Detail & Related papers  (2024-04-18T01:48:28Z)
• A Multilingual Perspective on Probing Gender Bias [0.0]
  Gender bias is a form of systematic negative treatment that targets individuals based on their gender. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies.
  arXiv  Detail & Related papers  (2024-03-15T21:35:21Z)
• Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications [0.0]
  The study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of Large Language Models. The paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques.
  arXiv  Detail & Related papers  (2023-07-18T11:38:45Z)
• Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects [7.175621752912443]
  We propose a causal research design for observational (non-experimental) data to estimate the natural direct and indirect effects of social group signals on speakers' responses. We illustrate the promises and challenges of this framework via a theoretical case study of the effect of an advocate's gender on interruptions from justices during U.S. Supreme Court oral arguments.
  arXiv  Detail & Related papers  (2021-09-15T19:15:35Z)
• Gender bias in magazines oriented to men and women: a computational approach [58.720142291102135]
  We compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group over a decade. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time.
  arXiv  Detail & Related papers  (2020-11-24T14:02:49Z)
• Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms [68.85295025020942]
  We propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a Search Engines to support gender stereotypes. GSR is the first specifically tailored measure for Information Retrieval, capable of quantifying representational harms.
  arXiv  Detail & Related papers  (2020-09-02T20:45:04Z)

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.