Reproduced the FAccT 2024 NSFW bias audit on a 5MB on-device YOLO model — lower demographic bias than 888MB CLIP models
Indie developer here. I built a custom YOLO26n NSFW detector (5.1MB, fully on-device) and reproduced the Leu, Nakashima & Garcia FAccT 2024 bias audit methodology against it.
Gender false positive ratio came out at 1.23× vs up to 6.4× in the audited models. Skin tone ratio 0.89× — near perfect parity.
My hypothesis is that anatomy detection is structurally less prone to demographic bias than whole-image classification — full methodology and benchmarks in the article.
Obvious caveat: I'm the developer. Independent replication welcome.
Posted Mar 8, 2026, 8:02 PM UTC
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