  Pre-trained language models learn socially harmful biases from their training
corpora, and may repeat these biases when used for generation. We study gender
biases associated with the protagonist in model-generated stories. Such biases
may be expressed either explicitly ("women can't park") or implicitly (e.g. an
unsolicited male character guides her into a parking space). We focus on
implicit biases, and use a commonsense reasoning engine to uncover them.
Specifically, we infer and analyze the protagonist's motivations, attributes,
mental states, and implications on others. Our findings regarding implicit
biases are in line with prior work that studied explicit biases, for example
showing that female characters' portrayal is centered around appearance, while
male figures' focus on intellect.
