Subtle innuendos are a major challenge for NSFW AI to detect. Although the models for detecting content are very advanced in AI, its ability to interpret double meaning sentences or jokes may not be as perfect. Human content moderation may be more time-consuming, but AI-based systems have a 90% accuracy rate in terms of efficiently catching clear explicit material (close to that responses from agents) and around just 65% for ambiguous language, according to Forbes.
The challenge in this case is primarily because of the way AI models interpret language, which originates into more complexity to detect innuendos. In fact, the great majority of NSFW AI systems are powered by Natural Language Processing (NLP), that can digest human language and convert into structured data for machine analysis. Where these models can effectively flag almost-celebrated terms or phrases, subtextual nuances demand grasping context, tone andor intent — components that AI doesn't have the intelligence to truly understand. For example, homographs can get by the filters; that's when different senses are associated with double meanings of a single word (for instance rose as a flower vs.rose color).
Sarcasm, or a euphemism (NSFW): A note of caution when using sarcasm as input for NSFAI. Oftentimes people will be more subtle as they hint at something and AI models that are trained on clear examples where everyone spells it out may have seen too few of these. Facebook, for example regularly used AI moderator often pass over content Hinting on TechCrunch article subtlety of automated systems can be problematic.
Explicit language identification, on the other hand, is quite easy for these models It is able to identify a number of terms with high accuracy based on the comparison it performs against its database, which has been pre-flagged. But as words are context-dependent, the model cannot rely on these simple algorithms to determine whether content needs flagging. A 2021 MIT study also pointed to the greater difficulty AI has with detecting innuendo over overt, explicit content: moderate errors for ambiguous language increased the error rate of an amount equivalent to expected absolute miscounts from a human by +20%.
Furthermore, NSFW AI is horny bots and hence many times on offense don't have the emotional intelligence to sense if something said in a conversation so it ends up becoming noise. Snap gets around the issue of having so much minutiae by leaning on AI, which — for a chatbot or any other computer interface — is an obvious solution (though one that prior iterations of these kinds have either eschewed completely in favor of human power or not succeeded at yet because the tech wasn't up to snuff).()]. The unsaid problem there is also an explanation about how this works well with text-based but would be put into even sharper relief if we were discussing actual speech. For all its parsing and ingesting prowess, Watson can still only do what it thinks you want based on language alone; imagine trying to get smart personal assistant-yness out your iOS voice command Siri thingy mediated sounds? Even in image and video formats, AI tends to focus more on pixel patterns than the human capability of emotions which can serve well for recognizing suggestive behavior.
As we can all remember — Elon Musk famously remarked, "AI is far more dangerous than nukes." Although he was making a general point about AI, this comment points to one of the challenges that show how limited is our interpretability in human space. However if AI is not smart enough to understand subtleties like innuendo, on the other hand it may either ignore inappropriate content or over-censor true conversation. This can result in an underperformance of content moderation, causing a discrepancy between user expectations and the AI.
First off, NSFW AI (which has a notoriously hard time catching innuendos) would have to become more intelligent through modern machine learning methods like contextual deep learning or an overhaul of the training dataset. Such models would have to be trained not only with explicit content but also nuanced vocabularies that many humans use when they try to hide their intent. The addition of cultural nuances and varying language patterns would improve an additional 15%, according to The Verge, but this technology is clearly in its infancy.
If you want to dive deeper into how nsfw ai works and its pros/cons, here is more on reading about it at nsfwai.com: Challenges of AI content moderation today & beyond