  Warning: this paper contains content that may be offensive or upsetting.
  Language has the power to reinforce stereotypes and project social biases
onto others. At the core of the challenge is that it is rarely what is stated
explicitly, but rather the implied meanings, that frame people's judgments
about others. For example, given a statement that "we shouldn't lower our
standards to hire more women," most listeners will infer the implicature
intended by the speaker -- that "women (candidates) are less qualified." Most
semantic formalisms, to date, do not capture such pragmatic implications in
which people express social biases and power differentials in language.
  We introduce Social Bias Frames, a new conceptual formalism that aims to
model the pragmatic frames in which people project social biases and
stereotypes onto others. In addition, we introduce the Social Bias Inference
Corpus to support large-scale modelling and evaluation with 150k structured
annotations of social media posts, covering over 34k implications about a
thousand demographic groups.
  We then establish baseline approaches that learn to recover Social Bias
Frames from unstructured text. We find that while state-of-the-art neural
models are effective at high-level categorization of whether a given statement
projects unwanted social bias (80% F1), they are not effective at spelling out
more detailed explanations in terms of Social Bias Frames. Our study motivates
future work that combines structured pragmatic inference with commonsense
reasoning on social implications.
