  Warning: this paper contains content that may be offensive or upsetting.
  Numerous natural language processing models have tried injecting commonsense
by using the ConceptNet knowledge base to improve performance on different
tasks. ConceptNet, however, is mostly crowdsourced from humans and may reflect
human biases such as "lawyers are dishonest." It is important that these biases
are not conflated with the notion of commonsense. We study this missing yet
important problem by first defining and quantifying biases in ConceptNet as two
types of representational harms: overgeneralization of polarized perceptions
and representation disparity. We find that ConceptNet contains severe biases
and disparities across four demographic categories. In addition, we analyze two
downstream models that use ConceptNet as a source for commonsense knowledge and
find the existence of biases in those models as well. We further propose a
filtered-based bias-mitigation approach and examine its effectiveness. We show
that our mitigation approach can reduce the issues in both resource and models
but leads to a performance drop, leaving room for future work to build fairer
and stronger commonsense models.
