Training Computers to See Internet Pornography: Gender and Sexual Discrimination in Computer Vision Science
By R. Gehl, Lucas Moyer‐Horner, Sara K. Yeo. 2017
Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- A. Hanna
- Emily L. Denton
- Inioluwa Deborah Raji
- Amandalynne Paullada
- Emily M. Bender
- Tarleton Gillespie
- Luc Van Gool
- Ming-Hsuan Yang
In Safety, Robustness & Interpretability
- Emily L. Denton
- A. Hanna
- Emily M. Bender
- Amandalynne Paullada
- Tarleton Gillespie
- Inioluwa Deborah Raji
- Emilio Ferrara
Citations in the corpus
- AI and the Everything in the Whole Wide World Benchmark (2021, background): “…in mainstream face datasets [Merler et al., 2019], and images of members of these communities are often tagged with racial or ethnic slurs, even on large general use datasets such as MIT Tiny Images or ImageNet [Crawford and Paglen, 2019, Gehl et al., 2017, Birhane and Prabhu, 2021].”
- Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020, background): “thereby implicitly assuming a relatively narrow and conservative view of pornography that happens to align with a straight male gaze.(25) In an examination of the person categories within the ImageNet dataset,(3) Crawford and Paglen(26) uncovered millions of images of people that had been labeled wi”
- Content moderation, AI, and the question of scale (2020, background): “An effective tool may learn to make the same kinds of distinctions as before (Binns et al., 2017; Gehl et al., 2017).”