UnQovering Stereotyping Biases via Underspecified Questions

• URL: http://arxiv.org/abs/2010.02428v3
• Date: Sat, 10 Oct 2020 01:48:31 GMT
• Title: UnQovering Stereotyping Biases via Underspecified Questions
• Authors: Tao Li, Tushar Khot, Daniel Khashabi, Ashish Sabharwal, Vivek Srikumar
• Abstract summary: We present UNQOVER, a framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors. We use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion.
• Score: 68.81749777034409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.

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