Content moderation using NSFW AI has improved significantly, but one of the challenges is its localization. NSFW stranger friendly AI — it has to adapt with cultural, linguistics and regional differences in algorithms. The European Commission reported in 2021, that what is considered potentially offensive content in one region and country could be wholly benign to another. Kumaran cited nudity or violence as two key content categories, the definitions of which vary hugely by territory – one thing that would cause a flag in one country may not raise any concerns in another, meaning a single AI might struggle to detect this correctly across jurisdictions.
As far as data is concerned, localization matters. In 2022 alone, McKinsey found that nearly four out of 10 AI failures in content moderation were the result of a lack of training data at local contexts. An AI model trained using mainly Western data may not identify culturally relevant shapes or language nuance in countries in Asia or Africa. This leads to a large number of false positives and negatives overtaking NSFW AI.
For instance, a government program to scale back internet censorship using AI in China in 2019 produced variable results. Localized content moderation attempted but was not as successful due to regional slang, politically sensitive material and local cultural taboos despite widespread investment into these AI tools. In 2020, India faced a similar problem where a regional AI system could not recognize obscene content as such, it was because certain local dialects and the use of their slang language which could not be recognized by AI.
Language processing is an important aspect for localizing NSFW AI. AI models, such as GPT-3 will learn many languages but without the contextual information input about regional dialects or local slang surrounding their usage, the process will be limited in accuracy. One of the biggest challenges arises due to data — since AI uses a lot of data for training, so lack of large & diverse dataset in non-English language is definitely a barrier.
Despite all this, most companies do localization to achieve precision. A 2023 report from the AI research firm, CognitionX showed that well over 60% of all AI companies are currently working on systems focused upon localised content moderation [14]. As an example nsfw ai translates, various languages and localizations are in the works.
Localized nsfw ai would then be able — but only with a high degree of localization sophistication, including an appreciation and discernment of local cultural nuances such as differences in: language nuance; profusely-genred regional content standards. While AI will improve and cater to different global needs, the quest for universal efficiency is still a work in progress.