Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion
  Related to Harms of Misinformation
Terrence Neumann Sooyong Lee Maria De-Arteaga Sina Fazelpour Matthew Lease
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.