Investigating Societal Biases in a Poetry Composition System

By Emily Sheng, David C. Uthus. 2020

68 citations recorded by the source corpus.

There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.

Subfields: Natural Language Generation, AI Ethics, Fairness & Society

Citations in the corpus

• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022, neutral): “We include 26 datasets as follows: fi-nancial_phrasebank (Malo et al., 2014), poem_sentiment (Sheng and Uthus, 2020), medical_questions_pairs (McCreery et al., 2020), glue-mrpc (Dolan and Brockett, 2005), glue-wnli (Levesque et al., 2012), climate_fever (Diggel-mann et al., 2020), glue-rte (Dagan et…”