DrawL: Understanding the Effects of Non-Mainstream Dialects in Prompted Image Generation

TL;DR

This paper investigates how non-mainstream dialects in prompts influence the portrayal of marginalized groups in text-to-image models, revealing that subtle syntax changes can systematically alter generated images' demographics.

Contribution

It demonstrates that dialectal variations in prompts can significantly affect image outputs, highlighting implicit biases and distribution shifts in text-to-image models.

Findings

Minimal syntax changes cause demographic shifts in images

Dialectal prompts influence skin tone and gender portrayal

Distribution shifts may be harmful or desirable depending on context

Abstract

Text-to-image models are now easy to use and ubiquitous. However, prior work has found that they are prone to recapitulating harmful Western stereotypes. For example, requesting that a model generate an "African person and their house," may produce a person standing next to a straw hut. In this example, the word "African" is an explicit descriptor of the person that the prompt is seeking to depict. Here, we examine whether implicit markers, such as dialect, can also affect the portrayal of people in text-to-image outputs. We pair prompts in Mainstream American English with counterfactuals that express grammatical constructions found in dialects correlated with historically marginalized groups. We find that through minimal, syntax-only changes to prompts, we can systematically shift the skin tone and gender of people in the generated images. We conclude with a discussion of whether dialectic distribution shifts like this are harmful or are expected, possibly even desirable, model behavior.