Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021)

Abstract: 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.

## introduction pre-trained language models (lms) @xcite @xcite have been successfully used in many nlp tasks including generation. despite their widespread usage, recent works showed that lms capture and even reinforce unwanted social stereotypes abundant in their training corpora @xcite @xcite @xcite . this phenomenon has also been observed with their predecessors, word embeddings @xcite @xcite . while many prior works have examined societal biases in specialized nlg systems such as dialogues systems @xcite @xcite , not much work has been done on bias analysis for story generation systems. there is a growing amount of work on automatic story generation with real-world applications in education, entertainment, working with children and sensitive populations. therefore, it is essential protagonist earned a college degree. ## data and processing pipeline in this section, we describe our data processing pipeline. we use gpt-2 @xcite as our underlying generation model given its recent success in story generation @xcite . we fine-tune gpt-2 to generate stories given titles on rocstories @xcite . rocstories is a collection of 98, 161 short stories. this dataset captures a rich set of causal and temporal commonsense relations between daily events, making it an ideal avenue to study bias. we follow the training settings of medium-size gpt-2 as in @xcite . at inference time, we generate stories using top-k sampling scheme @xcite with @xmath0.7. to quantify implicit gender bias, we create a pipeline to divide stories into two groups based on the protagonist's gender (section 2.1), and then extract pragmatic implications about the protagonist and others affected by them (section 2.2). our pipeline is described below and exemplified in figure 2. ## bias measurements we decompose examining the gender bias against the protagonist along the following three axes: ## conclusion automatic story generation has real-world applications in entertainment, training and educating children. hence, it is important for the generated stories to be socially unbiased. while biases can be expressed both explicitly and implicitly, this paper highlights the implicit gender bias in automatically generated stories. we devised a pipeline to uncover implicit gender bias about the protagonist in modelgenerated stories using a commonsense inference engine. we show that male and female protagonists are portrayed with certain stereotypes: male protagonists are portrayed as more intellectual, while female protagonists are portrayed as more sexual and beautiful. in terms of mental states, female protagonists are more positive than male protagonists when interacting with others. finally, we found protagonists' motives to be gendered as well. our method can be used during post-hoc analysis of automatic story generation systems to quantify the genderness of their generated stories. also, while designing gender-neutral models is out of the scope of the current paper, future work can use our findings to design unbiased story generators such as by using our experiments to design rewards for reinforcement learning models. lastly, following prior work, we analyze gender bias in a binary gender setup. a more realistic analysis, left for future work, should consider gender as a spectrum. we hope our study will encourage future work to devise methods for mitigating not just explicit but also implicit gender biases.