  AI models have become extremely popular and accessible to the general public.
However, they are continuously under the scanner due to their demonstrable
biases toward various sections of the society like people of color and
non-binary people. In this study, we audit three existing gender analyzers --
uClassify, Readable and HackerFactor, for biases against non-binary
individuals. These tools are designed to predict only the cisgender binary
labels, which leads to discrimination against non-binary members of the
society. We curate two datasets -- Reddit comments (660k) and, Tumblr posts
(2.05M) and our experimental evaluation shows that the tools are highly
inaccurate with the overall accuracy being ~50% on all platforms. Predictions
for non-binary comments on all platforms are mostly female, thus propagating
the societal bias that non-binary individuals are effeminate. To address this,
we fine-tune a BERT multi-label classifier on the two datasets in multiple
combinations, observe an overall performance of ~77% on the most realistically
deployable setting and a surprisingly higher performance of 90% for the
non-binary class. We also audit ChatGPT using zero-shot prompts on a small
dataset (due to high pricing) and observe an average accuracy of 58% for Reddit
and Tumblr combined (with overall better results for Reddit).
  Thus, we show that existing systems, including highly advanced ones like
ChatGPT are biased, and need better audits and moderation and, that such
societal biases can be addressed and alleviated through simple off-the-shelf
models like BERT trained on more gender inclusive datasets.

Computers and Society;Computation and Language