  Existing word embedding debiasing methods require social-group-specific word
pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which
cannot be used to mitigate bias for other social groups, making these methods
impractical or costly to incorporate understudied social groups in debiasing.
We propose that the Stereotype Content Model (SCM), a theoretical framework
developed in social psychology for understanding the content of stereotypes,
which structures stereotype content along two psychological dimensions -
"warmth" and "competence" - can help debiasing efforts to become
social-group-agnostic by capturing the underlying connection between bias and
stereotypes. Using only pairs of terms for warmth (e.g., "genuine"-"fake") and
competence (e.g.,"smart"-"stupid"), we perform debiasing with established
methods and find that, across gender, race, and age, SCM-based debiasing
performs comparably to group-specific debiasing
