AI is finding an increasing application in identifying unusual patterns within online content, specifically to root out harmful, inappropriate and offensive material. This anomaly detection capability is critical for moderation of digital platforms that have a high volume of user-generated content uploaded daily. For example, AI systems continuously scan millions of images and texts an hour, using complex algorithms to detect abnormal patterns. According to the study by University of Washington 2021, AI tools were able to recognize the data for inappropriate content with more than85% precision and flag unusual patterns from text area or image data even before any human content moderator was present.
More subtler: Practically speaking, NSFW AI employs machine learning to constantly learn how to detect these subtle changes as user behaviors or writing styles change. As an example, Instagram uses AI-based tools to detect increases in hate speech or aggressive language. Facebook flagged more than 1.7 billion pieces of content for violating community standards in the first half of 2021, and the bulk of that success could be attributed to A.I. identifying patterns of abuse, as seen in Facebook’s Transparency Report. The AI models monitor for repeated actions such commenting using specific language that is defined as harm or altering image in such a way it targets a particular person, all which signal atypical behavior compared to regular non-offensive social media activity.
Further, NSFW AI are able to identify anomalies in content creation or consumption behaviour. According to a report from the European Commission, AI can detect the point at which users start creating or sending abnormal content — for example, if within minutes they produce significant quantities of sexually explicit images or derogatory comments. AI systems can identify irregularities like these that would be difficult for humans to find in real-time and flag them for further examination. In one Twitter case study, the AI tools were able to increased harmful content given a profile event of 45% and as such human moderators intervened in real-time.
AI, for example, would notice that users are starting to behave in a suspiciously allied way and start ‘clustering’ them into groups of connected accounts — posting abusive or harmful content. The same method is employed by cybersecurity companies to find out fraud or phishing, and spotting abnormal exercise patterns are flagged in real-time. In the words of Facebook founder Mark Zuckerberg, “We are becoming better at finding and removing harmful content through AI but still working on it. We need to identify these patterns more quickly and with better accuracy”
AI systems also learn from incoming data to improve their detection capabilities. YouTube said its artificial intelligence tools now catch more than 70% of bad content before any human ever sees it, an increase from 60% in the prior year, according to a report released in 2020. Because they are driven by deep-learning algorithms — especially improvements in neural-network architecture — they can recognize both blatant and subtle abuse.
NSFW AI has proven to be an incredibly useful tool in the fight against harmful and inappropriate online content by detecting anomalies. This adaptability from the data the NLP processes allows it to be a powerful moderation tool for our digital platforms. To learn more about how AI is used for harmful contents detection, check nsfw ai.