Correct Representation in Datasets
One of the biggest problems in training NSFW AI for other cultures, ethnic backgrounds, and other societies is in making sure the data sets being used are diverse and is a good representative of the above. Data sets often represent the demographic that made them- usually, Western users. For example, research has revealed that the misjudgment rate among many AI-driven content moderation systems is higher for examining posts from non-Western cultures owing to lack of inclusion from the learning sequence. While increasing the diversity in the data set can alleviate some of these errors, acquiring and curating such diverse data is a laborious and costly process.
Contextual Nuances Are Key
Inappropriate content can only be determined in context, and this gets even more difficult for an AI where cultural and social backgrounds are completely diverse. One culture could be deeply offended by a phrase or image that is completely acceptable elsewhere. For example, in some areas of the Middle East and West Africa, a thumbs up can be considered rude. Not only does training NSFW AI to understand these nuances require diverse data, it also demands sophisticated algorithms that can accurately interpret context - still a developing area in AI technology.
Legal and Ethical Compliance
This layer of complexity of training NSFW AI raises even more challenges in compliance with legal and ethical standards in different regions. What would constitute overreach in some jurisdictions, are enforced as informational blackouts in others. An example would be hate speech laws in European countries (they are more directly enforced when compared to the U.S. where free speech is aggressively emphasized). Facilitating the compliance of AI for NSFW under these different mandates while maintaining its efficacy for content moderation remains a heavy endeavor; one that needs a regular check and a dedicated process for legal review and adapting the AI protocols.
This relates to the ability of the technology to easily scale up and down processing as demand changes and the speed with which these transactions are processed.
While not all NSFW AI systems are trained to support such a variety of people, the increased complexity of those AI systems being trained this way can have implications for their performance and scalability. The processing of large and diverse datasets and the implementation of sophisticated contextual analyses require computational power and optimized algorithms. More difficult for young companies and smaller platforms that lack the expertise and funds. According to some tech developers (sorry, but this is just hilarious), developing NSFW AI systems which demand more complexity could add 30-50% on top of existing operational costs... commmmmooooon!
Fairness in AI-Learning from biased decisions!
Given the diversity of the population the FAIRNESS (and limiting bias) in the decision making of NSFW AI is paramount. An AI system can also inadvertently reinforce existing stereotypes or biases present in the training data. This AI, for example, will have the Western beauty standards as the main training image, and so can also wrongly identify beauty representation where various portrayals as their incorrect designation. While auditing and updating AI models continuously to account for and mitigate these biases is critical, it is also incumbent upon the practitioner to keep pace with the prevailing concern and quickly adapt.
Visit nsfw ai for more on how these challenges can inhibit the efficacy and t fairness of NSFW AI in various settings. If we are to build AI systems of technical excellence and social and ethical propriety, these challenges must be overcome.
