Further to my post about Facial Recognition Software, I have considered the work of Trevor Paglen.
Paglen’s work “Machine-Readable Hito” uses a face-analysing algorithm on hundreds of images of fellow artist Hito Steyerl. In each image, she has a different facial expression or angle of her face relative to the camera. The software then analysed each face. Paglen presented the images with an annotation underneath of the software’s prediction for age, gender, and emotional state.
In one of the images, she is estimated to be 74% female.
“It’s an absurd but simple way to raise a complicated question: Should computers even attempt to measure existentially indivisible characteristics like sex, gender, and personality—and without asking their subject? (Secondarily, what does 100% female even look like?)”
– (Hu 2018)
Figure 1: Trevor Paglen. 2017. Machine Readable Hito “A Study of Invisible Images.”
Figure 2: Trevor Paglen. 2017. Machine Readable Hito, adhesive wall material.
Paglen’s work is very similar to that of Clément Lambelet (previous post). I viewed Lambelet’s work at Unseen in 2017.
REFERENCES
Hu, C. 2017. “The secret images that AI use to make sense of humans”. Quartz [online]. Available at: https://qz.com/1103545/macarthur-genius-trevor-paglen-reveals-what-ai-sees-in-the-human-world/ [accessed 25 June 2018].
IMAGE SOURCES
Figure 1: Hu, C. 2017. “The secret images that AI use to make sense of humans”. Quartz [online]. Available at: https://qz.com/1103545/macarthur-genius-trevor-paglen-reveals-what-ai-sees-in-the-human-world/ [accessed 25 June 2018].
Figure 2: machine readable – new york art tours. 2017. Newyorkarttours.com[online]. Available at: http://newyorkarttours.com/blog/?tag=machine-readable [accessed 25 June 2018].
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