  Stereotypical language expresses widely-held beliefs about different social
categories. Many stereotypes are overtly negative, while others may appear
positive on the surface, but still lead to negative consequences. In this work,
we present a computational approach to interpreting stereotypes in text through
the Stereotype Content Model (SCM), a comprehensive causal theory from social
psychology. The SCM proposes that stereotypes can be understood along two
primary dimensions: warmth and competence. We present a method for defining
warmth and competence axes in semantic embedding space, and show that the four
quadrants defined by this subspace accurately represent the warmth and
competence concepts, according to annotated lexicons. We then apply our
computational SCM model to textual stereotype data and show that it compares
favourably with survey-based studies in the psychological literature.
Furthermore, we explore various strategies to counter stereotypical beliefs
with anti-stereotypes. It is known that countering stereotypes with
anti-stereotypical examples is one of the most effective ways to reduce biased
thinking, yet the problem of generating anti-stereotypes has not been
previously studied. Thus, a better understanding of how to generate realistic
and effective anti-stereotypes can contribute to addressing pressing societal
concerns of stereotyping, prejudice, and discrimination.
