More of the Same: Persistent Representational Harms Under Increased Representation
Jennifer Mickel, Maria De-Arteaga, Liu Leqi, Kevin Tian
To recognize and mitigate the harms of generative AI systems, it is crucial to consider who is represented in the outputs of generative AI systems and how people are represented. A critical gap emerges when naively improving who is represented, as this does not imply bias mitigation efforts have been applied to address how people are represented. We critically examined this by investigating gender representation in occupation across state-of-the-art large language models. We first show evidence suggesting that over time, there have been interventions to models altering the resulting gender distribution, and we find that women are more represented than men when models are prompted to generate biographies or personas. We then develop a methodology for understanding representational differences between groups. We use this methodology to demonstrate that representational biases persist in how different genders are represented by examining statistically significant word differences across genders. This results in a proliferation of these representational differences, some of which are associated with representational harms and stereotypes, despite existing interventions to increase female representation, which can reinforce these harms.