Yes, the NSFW AI chat systems can get better with the user feedback and this helps in making your system more proficient in detection as well as moderation. To be precise, AI models —particularly with machine learning as a subset of AI type— stand to gain up to 25% better performance when trained against real-time human input. And that is the strategy used by platforms like Facebook and Twitter to improve their content moderation systems. Users can report any false-positive cases of citing some harmless content or cases where they miss the dangerous content and use this DAta to train its AI systems. For example, after incorporating a flagged feedback feature to its AI chat tools to allow users to mark flagged content as false, Facebook was able to improve its detection of offensive language by 18% simply by knowing whether or not the AI was correct in its judgment on whether content used words that violate Facebook's terms.
There are a few forms of feedback that AI models can process, such as consumer ratings, manual content moderation reviews, and behavioral analysis. For example, Google, a household name in the tech industry, announced that its content moderation AI was 30% more accurate thanks to user feedback loop implemented in 2021. By giving feedback on flagged content, the system was better able to learn context and the difference between harmful/benign language — and this mouth of the loop. This resulted in pleasing customers and minimize false positives.
When it comes to what constitutes NSFW content, AI chat systems targeted specifically toward this sort of material benefit tremendously from feedback — particularly when the lingo used is more nuanced with slang that AI may not be familiar with immediately. In 2022, AI Monitoring Solutions reported that systems used by adult websites were able to learn from the reports generated by the users: such systems can further refine their language models to tell when conversations are contextually appropriate or inappropriate. So, for instance, if a user marked an interaction as unacceptable, then then the system would catch that data point and it woould relust in changes to its algorithm and adjustments based on that learning would impact future interactions.
In addition, through some techniques, businesses can input their feedback for the AI so that it learns to be more in-line with what the business-specific policies are as well as what users typically prefer. OpenAI, among others, provides customizable AI chat models which enable businesses to adjust the behavior of an AI based on user input. Training the model on these datasets that were collected using user feedback allows for the system to be more proficient in identifying what is considered unacceptable or acceptable by an average user.
But none of that mitigates the user feedback makes NSFW AI chat systems so much better deal breaker. In vocal communities, feedback can be contradictory or ineffective as one user views content inappropriately and others do not. For example, an Institute for AI Ethics study identified over 20% of feedback being misleading or misinterpreted by the AI in online platforms that analyse thousands of sentences requiring correction thereby making the correction learning process inefficient [10]. This implies a never-ending supply of adaptations in feedback present systems geared towards ensuring that the learning played by AI is consistent with wider societal and ethical constraints.
This, alongside other works [9, 10], prove that NSFW AI chat systems are capable of leaning from user feedback and so have the potential to adapt and evolve in response to interactions sua syllabus with real-world users. Introducing feedback loops is an established way to help the AI better detect inappropriate things and improve the user experience. Read more at Sex Chat Creepypasta nsfw ai chat.