TY  - JOUR
TI  - Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes
AU  - Bianchi, F.
AU  - Kalluri, P.
AU  - Durmus, E.
AU  - Ladhak, F.
AU  - Cheng, M.
AU  - Nozza, D.
AU  - Hashimoto, T.
AU  - Jurafsky, D.
AU  - Zou, J.
AU  - Caliskan, A.
PY  - 2023
DA  - 2023///
JO  - ACM FAccT 2023
AB  - 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.
KW  - training-data
KW  - ai-literacy
KW  - student-experience
UR  - https://arxiv.org/abs/2211.03759
LA  - en
ER  -