Character (mathematics)

Linguistics

Philosophy

Similarity (geometry)

Social psychology

Narrative

Computer science

Lexicon

Natural language processing

Psychology

Mathematics

Politics

Image (mathematics)

Political science

Epistemology

Storytelling

Gender bias

Artificial intelligence

Geometry

Representation (politics)

Word (group theory)

Unintended consequences

Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes.Generated stories depict different topics and descriptions depending on GPT-3's perceived gender of the character in a prompt, with feminine characters 1 more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt.Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.

Gender and Representation Bias in GPT-3 Generated Stories