@article{texttoimagestereotypes2023,
  title = {Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes},
  author = {Bianchi, F. and Kalluri, P. and Durmus, E. and Ladhak, F. and Cheng, M. and Nozza, D. and Hashimoto, T. and Jurafsky, D. and Zou, J. and Caliskan, A.},
  year = {2023},
  journal = {ACM FAccT 2023},
  url = {https://arxiv.org/abs/2211.03759},
  abstract = {Bianchi and colleagues' paper, published at ACM FAccT 2023, provides systematic empirical evidence that widely accessible text-to-image generation systems amplify demographic and cultural stereotypes at scale. Generating images from neutral occupational and social prompts, the study documents consistent over-representation of specific demographic groups and under-representation of others across models. The paper is a standard citation in the bias and representation literature and provides the kind of peer-reviewed empirical grounding that educators need when teaching equity and diversity concerns around generative AI in creative practice.},
  keywords = {training-data, ai-literacy, student-experience},
  note = {AI \& Animation Education Knowledge Base}
}