AI in the Gray: Exploring Moderation Policies in Dialogic Large Language
  Models vs. Human Answers in Controversial Topics
Vahid Ghafouri Vibhor Agarwal Yong Zhang Nishanth Sastry Jose Such Guillermo Suarez-Tangil
The introduction of ChatGPT and the subsequent improvement of Large Language
Models (LLMs) have prompted more and more individuals to turn to the use of
ChatBots, both for information and assistance with decision-making. However,
the information the user is after is often not formulated by these ChatBots
objectively enough to be provided with a definite, globally accepted answer.
  Controversial topics, such as "religion", "gender identity", "freedom of
speech", and "equality", among others, can be a source of conflict as partisan
or biased answers can reinforce preconceived notions or promote disinformation.
By exposing ChatGPT to such debatable questions, we aim to understand its level
of awareness and if existing models are subject to socio-political and/or
economic biases. We also aim to explore how AI-generated answers compare to
human ones. For exploring this, we use a dataset of a social media platform
created for the purpose of debating human-generated claims on polemic subjects
among users, dubbed Kialo.
  Our results show that while previous versions of ChatGPT have had important
issues with controversial topics, more recent versions of ChatGPT
(gpt-3.5-turbo) are no longer manifesting significant explicit biases in
several knowledge areas. In particular, it is well-moderated regarding economic
aspects. However, it still maintains degrees of implicit libertarian leaning
toward right-winged ideals which suggest the need for increased moderation from
the socio-political point of view. In terms of domain knowledge on
controversial topics, with the exception of the "Philosophical" category,
ChatGPT is performing well in keeping up with the collective human level of
knowledge. Finally, we see that sources of Bing AI have slightly more tendency
to the center when compared to human answers. All the analyses we make are
generalizable to other types of biases and domains.