Companies will have to weigh the trade-offs between efficiency, accuracy, scalability and ethics in deciding which NSFW AI solution they employ. This is influenced by a mix of factors, such as the scale at which we have to rapidly process user-generated content (UGC), accuracy requirements and our stated standards for maintaining minimal harmful – safe and respectful platform. This is how AI solutions are selected — from quality and operational costs to other criteria, the system must be able to handle a lot of content while maintaining high-quality.
The first consideration that must be made is precision. NSFW AI systems usually target around 85-90% accuracy in detecting nudity, as reported by MIT Technology Review. Whilst this percentage may seem low, the remaining 10–15% margin of error means that harmful content can slide through or inoffensive material be removed by mistake. When you host millions of items per day, as YouTube does with 500 hours or video being uploaded every minute, these systems have to be incredibly accurate in order not to let through too much objectionable content or block the wrong things. Therefore many companies choose the AI solutions that show they are able to learn from their mistakes and get better through experience.
The other vital component is velocity. Facebook relies on real-time AI systems that can scan millions of posts and images per second. Acquiring NSFW AI solutions should be able to identify provocative content immediately within 1 millisecond in order that erotic or pornographic content will not see by the majority earlier than moving it. This requirement of speed is not all that significant, since most popular and in availability platforms actively monitoring messages flow. And the Facebook Community Standards Report reveals that 99% of harmful content on their site is removed by AI detection within seconds, long before human moderators even have a chance to review it.
And, of course scalability. So, as a platform scales up in size (and begins to produce more media), the AI-based solutions it implements must be built for reasonable performances even when faced with much higher content volumes. This need systems that use machine learning and deep-learning techniques built on reliable mechanisms to recognize NSFW material of various kinds as they evolve. Most companies are looking for AI systems that can be adapted to new contexts thus scaling up with stable performance without human intervention. Despite their limitations, they are popular due to the scalability of cloud-based solutions that allocate more or less computational power as per demand.
Apart from the numbers, ethical side-determinants have a pivotal role in taking up any decision. There is growing recognition among companies that AI systems are biased, for example around the way in which these might unfairly label content depending on its race, sex or cultural background. A paper by Georgetown University found that AI moderation systems tend to tag the content reported most heavily as inappropriate more often — which can disproportionately impact marginalized groups and further raises questions about fairness in general. Marking the January news OpenAI CEO Sam Altman argued that ethical dimension on AI systems is imperative but also difficult to avoid and require a conscious effort [trigger] so as not to sell the existing problem of human prejudice deeply buried in automatic_pic_processing. Companies typically seek AI solutions that have been trained on a wide variety of data sets and are continuously kept current to prevent bias.
Another aspect is that of cost efficiency. Premium NSFW AI Systems — At the very best, one can get extremely accurate results fast but this comes with a cost signal. The cost of implementing these AI systems versus operational budgets. At the higher-end, Facebook is said to spend billions each year on developing AI and moderation tools; smaller platforms might prefer cheaper albeit less accurate offerings. Open source NSFW AI models, like the ones developed by TensorFlow can also provide a less expensive option to enterprise class implementations while still allowing for some level of customization.
In addition, companies are studying the effects on user experience of NSFW AI as well. A tendency to over-censor will frustrate users when harmless content is mistakenly flagged and under-censoring can see harmful material slip through the net. Balance is key. A platform like Twitter, for instance, has been slammed from both ends of the spectrum — with some crying foul when algorithms censor their posts and others claiming that it hasn't done enough to protect them against harmful content.
In the end, it is a business decision facing some trade-offs like accuracy-speed-scalability-ethical concern-cost and choose an NSFW AI that works best for their platform. One thing is for certain: As AI progresses, businesses will have to revisit their approaches in order to deal with new trends and problems facing content moderation. If you want to know more in detail about how this technology works and how platforms are the future of online content check out nsfw ai, which talks specifically on AI-driven solutions.