Mian Wei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mian Wei has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Liwei Shen
School of Journalism and Mass Communication, University of Wisconsin-Madison, USA
1 shared publication - Luhang Sun
University of Wisconsin-Madison, USA
1 shared publication - Sijia Yang
School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, United States
1 shared publication - Yibing Sun
University of Wisconsin–Madison, USA
1 shared publication - Yoo Ji Suh
School of Journalism and Mass Communication, University of Wisconsin – Madison, USA
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 8 November 2023
- Published by Oxford University Press (OUP) in Journal of Computer-Mediated Communication
- Vol. 29 (1)
Smiling women pitching down: auditing representational and presentational gender biases in image-generative AI
Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.
- 8 November 2023
- Published by Oxford University Press (OUP) in Journal of Computer-Mediated Communication
- Vol. 29 (1)
Top Cited PapersOpen AccessSmiling women pitching down: auditing representational and presentational gender biases in image-generative AI
Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.