The complexities of the human condition and variance from one culture to another combined with technical restrictions make NSFW AI development particularly challenging. The root issue here is in data collection and labeling itself. NSWF AI has massive datasets, which can consist of millions of images and videos tagged as explicit or safe. Yet due to the ethical and legal restraints of connecting identity with explicit content it is harder than imaginable finding this data in such a way where privacy or even legality are not compromised. In addition, companies spend millions of dollars every year on annotating these datasets effectively and it usually cost more than $50K per dataset with high-quality annotations. This cost metastasizes because with each misclassification, the AI overfits and becomes 30 percent less efficient.
Even more tricky, global differences in cultural standards for explicit content will come into the mix when building NSFW AI. A material inappropriate in one text might be appropriate for others from another region which means the solutions to auto-filter by AI will have to understand various nuances. For platforms that operate at the scale of Facebook, which has 2.9 billion monthly active users across all its apps and services worldwide, these nuances must be considered to effectively moderate content in fair and consistent ways. The challenge will come in developing “flexible” algorithms that can understand content context, something only possible with advanced natural language processing (NLP) models and constant re-training – a process which may add months if not more to the development timeline.
Adversarial attacks are also a problem with NSFW AI models, where changes of just a few pixels can trigger misclassifications. These slight changes, as shown by a 2020 MIT study, may result in up to a 45% error rate when attempting to identify explicit content. This vulnerability pressures companies into much more robust model defenses, which eat away at development time and dollars while also requiring continual observation to keep pace with the ever-changing attack tactics. Which brings me to quote Elon Musk “The pace of progress in artificial intelligence (I am not referring to narrow AI) is incredibly fast.” This can be said about the constant arms race required to lead NSFW Ai technology.
Bias in NSFW AI systems is still a lingering problem, too; these are presumably similar to biases that are already present inside the training data. As a result, this bias may cause biased moderation that will source content from certain demographics. These biases have been under heavy criticism by major companies, leading Facebook and Google to spend millions in efforts of reducing bias in AI models. Unbiased NSFW AI can only be learned on proper diverse datasets, but such data are very rare and creating a new one is not feasible. Not some, but a significant number of AI ethicists maintain that companies should allocate no less than 10% of their project budgets to fix such biases.
Real-time content analysis represents even another layer of technical complexity. One problem is that the NSFW AI has to work quickly, since some of these platforms (Twitter sees an estimated 500 million tweets daily!) are high-traffic. Ensuring performance at the required scale and speed is a massive computational task, often involving costs of over $100K/year for high-performance hardware setups. To solve this requirement, companies tend to use sophisticated machine learning techniques such as convolutional neural networks (CNNs), although the profile is so high that it requires significant resources.
For more information on the evolution and innovation of NSFW AI, you may want to read about how companies are tackling these everlasting concerns by nsfw ai. Given the mishmash of constraints from data to culture, developing NSFW AI calls for a relentless cycle of give-and-take which speaks volumes about just how nuanced this balancing act is between technology and content moderation we ride on top.