Using AI-backed content moderation can scale and improve user experiences across your entire platform. It can help you keep your community safe from harmful behavior and protect your brand reputation.
The key to a successful AI-based moderation strategy is operational precision, or the number of instances that are tagged as harmful without mistakenly flagging benign content. The best way to measure this is to analyze agreement fractions from your human moderators over time.
The vast amount of visual media that is uploaded to online platforms needs to be scanned for toxic content. Computer vision can be used to identify potential harmful material in images and videos, helping to keep communities and platforms safe from graphic violence and sexually explicit content.
It is also possible for computer vision to be used in tandem with natural language processing to transcribe and evaluate text within visual media. This allows for proactive moderation of user-generated content (UGC), including proactively flagging a range of potentially harmful speech and actions, such as cyberbullying and hateful language.
While generative AI tools like ChatGPT and Bard have gained a lot of attention for their efficiency and creativity, other forms of artificial intelligence are vital to the successful implementation of content moderation systems. Spectrum Labs’ content moderation AI models are tuned through active learning cycles, which include customer feedback, moderator action (e.g. de-flagging a piece of text that was incorrectly flagged as profanity), and language models being updated with emerging slang and connotations.
Natural Language Processing
While ML algorithms and machine learning have received much attention, natural language processing (NLP) is also crucial for AI content moderation. NLP is used to understand the meaning of words and sentences and to identify and classify offensive content, like hate speech or extremist threats. NLP tools use a combination of text analysis and lexicon knowledge to evaluate user-generated content for harmful or unwanted material.
NLP can be combined with other types of ai to optimize moderation processes, such as computer vision and voice analysis. For example, NLP could transcribe a video or photo into text for further evaluation using image classification. This enables companies to scale their online communities by automatically screening content submissions for harmful or unwanted material.
A growing number of Trust & Safety teams are deploying adaptable AI content moderation solutions to help them keep their users safe and provide an optimal online experience. These solutions vary in complexity and scope, but they generally fall into one of three categories: word filters and regex solutions, classifiers, and contextual AI.
The growth of user-generated content on social media platforms makes it difficult for human moderators to keep up. Using AI to automate the moderation process helps to reduce the workload and ensures that platform users are protected from harmful content.
Computers don’t suffer from mental fatigue and they can scan images, text, and videos faster than humans. This allows for near-instantaneous monitoring and identification of potentially problematic content 24 hours a day, 7 days a week.
As a result, AI can help to prevent legal and reputational damages that may stem from unmoderated UGC. In addition, AI-powered moderation can improve scalability by reducing the need for additional moderators.
Moderation tools that use AI can also be more accurate than manual processes. However, a number of challenges exist with the implementation of AI-powered moderation, including the difficulty in defining “toxic.” For example, one tool has struggled to determine whether leet speak (l337/leet), an online code used by hackers to conceal their identities, is harmful or not.
When a user uploads content, AI algorithms automatically check it for harmful or inappropriate text, visuals and videos. This helps maintain a positive online community by instantly removing toxic content, ensuring users have an enjoyable experience and protect their mental health.
For example, Amazon’s Rekognition can detect and remove explicit nudity or suggestiveness on photos at an 80% accuracy rate without human review. This reduces workload for moderators and enables platforms to keep up with the increasing volume of user-generated data.
When deploying an AI for content moderation, it’s important to choose the right model for your business and its users. The most effective models are those that achieve high operational precision. This means that they accurately identify harmful content, but also avoid mistakenly flagging benign content. Spectrum Labs uses supervised machine learning to train their algorithms to recognize harmful behaviors, like bullying and harassment. The algorithms are then tested and evaluated by a team of vetted human moderators.