Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language Models
arxiv(2023)
摘要
Groundbreaking inventions and highly significant performance improvements in
deep learning based Natural Language Processing are witnessed through the
development of transformer based large Pre-trained Language Models (PLMs). The
wide availability of unlabeled data within human generated data deluge along
with self-supervised learning strategy helps to accelerate the success of large
PLMs in language generation, language understanding, etc. But at the same time,
latent historical bias/unfairness in human minds towards a particular gender,
race, etc., encoded unintentionally/intentionally into the corpora harms and
questions the utility and efficacy of large PLMs in many real-world
applications, particularly for the protected groups. In this paper, we present
an extensive investigation towards understanding the existence of "Affective
Bias" in large PLMs to unveil any biased association of emotions such as anger,
fear, joy, etc., towards a particular gender, race or religion with respect to
the downstream task of textual emotion detection. We conduct our exploration of
affective bias from the very initial stage of corpus level affective bias
analysis by searching for imbalanced distribution of affective words within a
domain, in large scale corpora that are used to pre-train and fine-tune PLMs.
Later, to quantify affective bias in model predictions, we perform an extensive
set of class-based and intensity-based evaluations using various bias
evaluation corpora. Our results show the existence of statistically significant
affective bias in the PLM based emotion detection systems, indicating biased
association of certain emotions towards a particular gender, race, and
religion.
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