The issue of whether NSFW AI can take the place of censorship is a thorny and multi-layered question. We then exploit the Fastai and Efficient Net architecture for NSFW AI (Slightly) trained solely by ImageNet weights to exercise state-of-the-art detection of unsafe content, demonstrating better test results than traditional censorship methods. For example, human moderators can go through 200-500 pieces of content per hour while NSFW AI can process thousands and thousands of images or texts in an instant which ultimately reduces time & cost spent on moderation by approximately up to 90%. Yet, that does not mean it can fully replace human-led censorship.
NSFW AI usually works on algorithms that have been trained with large amounts of data — 1000s and even millions samples showing what is inappropriate. Some models can be very precise and are approaching 98% accuracy at detecting adult content. But if the ai-in-charge has problem understanding 2%, it can lead to crafting highly incorrect messages, probably misunderstanding due to context or culture nuances. A popular social media platform in 2021 faced heat when its NSFW AI banned an explicit photo of a classic work, bringing attention to what happens if you hire only robots for censorship.
Human judgment is powerful in censorship. The problem with NSFW AI is that censorship often requires a tacit cultural and context knowledge; it would be hard for an automated tool to completely understand every single case this takes place in. This challenge is well-expressed by a famous quote of former Google CEO Eric Schmidt: “The real danger is not that computers can think like men, but the true problem stems from whether or not us humans still do. This supports the broader discussion about how much AI can be allowed to make complex, and above all human-like decisions in delicate subjects such as censorship.
The trade also has financial implications to it. At the same time, developing and maintaining NSFW AI systems can also become a massive project, easily costing many organizations more than $10 million annually for AI-powered content moderation solutions. At the other end of spectrum, unless you operate with very low labour costs in your region it may be more cost effective for censorship through traditional hypercuration and moderation. Additionally, the cost of AI error — whether it come in censoring legit content or failing to detect toxic material there in general — can be severe; costing companies money & legal judgments and their reputation.
Real-world examples provide more proof of the impracticality in using NSFW AI as a substitute for censorship. Social media platforms turned to AI in a big way during the 2020 US presidential election -- think content moderation and misinformation filtering capabilities. However, the fact that numerous instances of false information still made their way to readers led to backlash and demands for greater human oversight. That is to say, it shows that work like NSFW AI can augment censorship efforts while suggesting that such a thing is unreliable enough not be able replace human judgment entirely.
The quickness and range of the NSFW AI have obvious applications in censorship, especially for platforms dealing with massive user-generated content. AI is ill-suited to the nuanced understanding that many forms of censorship need, especially where cultural/ethical/legal considerations are involved. NSFW AI integration to help in censorship practices can be an aid that boosts human capacity with speed and consistency, but must not serve as a one-stop solution.
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