  As large language models (LLMs) advance to produce human-like arguments in
some contexts, the number of settings applicable for human-AI collaboration
broadens. Specifically, we focus on subjective decision-making, where a
decision is contextual, open to interpretation, and based on one's beliefs and
values. In such cases, having multiple arguments and perspectives might be
particularly useful for the decision-maker. Using subtle sexism online as an
understudied application of subjective decision-making, we suggest that LLM
output could effectively provide diverse argumentation to enrich subjective
human decision-making. To evaluate the applicability of this case, we conducted
an interview study (N=20) where participants evaluated the perceived
authorship, relevance, convincingness, and trustworthiness of human and
AI-generated explanation-text, generated in response to instances of subtle
sexism from the internet. In this workshop paper, we focus on one troubling
trend in our results related to opinions and experiences displayed in LLM
argumentation. We found that participants rated explanations that contained
these characteristics as more convincing and trustworthy, particularly so when
those opinions and experiences aligned with their own opinions and experiences.
We describe our findings, discuss the troubling role that confirmation bias
plays, and bring attention to the ethical challenges surrounding the AI
generation of human-like experiences.
