In the blog, I suggest this could be the result of an uncultured data set when training the CNN. Or perhaps the dataset was fine, and this is pushing the hard limit of what ResNet50 CNN architecture can do (the off the shelf model I use for this is an extension of ResNet50).
Some of the anomalous results are amusing. One day, I uploaded a video of a female violinist in concert, and the model flagged every close up of her as NSFW! Just those closeups. Wide shots, and closeups of other musicians were absolutely fine.
Again some of that might be down to me (clunky code / very low NSFW threshold). And I suspect the model I used was itself a PoC (https://github.com/emiliantolo/pytorch_nsfw_model). But it does make you wonder how the bigger labs with critical products, like Palantir, handle doubts like this.
Watching the olympics at lunchtime is commonplace and accepted in most workplaces. Being a creep about it is not. It's almost always a pretty clear line between the two.
It only becomes sexualized by the behavior of the people watching - professional athletes competing for their country isn't something intrinsically inappropriate.
If someone is sexualizing something in the workplace, the problem is the person's behavior, not the thing they are sexualizing.
The situation we find ourselves in came from setting boundaries around content instead of behavior. Our society treats nudity itself as if it were the act of sexualization, assuming that people have no control over their own behavior.