  A common limitation of diagnostic tests for detecting social biases in NLP
models is that they may only detect stereotypic associations that are
pre-specified by the designer of the test. Since enumerating all possible
problematic associations is infeasible, it is likely these tests fail to detect
biases that are present in a model but not pre-specified by the designer. To
address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers
about PeOPle) in social commonsense question-answering. Our pipeline generates
modified instances from the Social IQa dataset (Sap et al., 2019) by (1)
substituting names associated with different demographic groups, and (2)
generating many distractor answers from a masked language model. By using a
social commonsense model to score the generated distractors, we are able to
uncover the model's stereotypic associations between demographic groups and an
open set of words. We also test SODAPOP on debiased models and show the
limitations of multiple state-of-the-art debiasing algorithms.
