AI for content moderation uses various kinds of automated technologies to identify harmful or unwanted behavior. They include machine learning algorithms, natural language processing, computer vision and voice analysis.
Despite their many advantages, these automated tools still have limitations. One major issue is that they often lack the ability to contextualize content. For example, a comment that’s considered “toxic” in one context may be harmless in another.
Machine learning
ML-powered content moderation removes items that are illegal, graphic, or noncompliant with a platform’s rules. This process takes less than 20 milliseconds and reduces the workload of human moderators. It also reduces the likelihood of legal compliance issues and unregulated exposure for users.
In addition to the machine learning used by the Spectrum Labs Guardian solution, a robust moderation platform must use multiple tools that help Trust & Safety teams identify harmful content quickly. These include word filters and RegEx solutions, classifiers, and contextual AI. These tools can be used as pre-moderation, or post-moderation.
These tools ensure that any content posted online is not in breach of community rules. Additionally, they manage demographic bias by ensuring that the data fed to models is curated and diverse. In this way, they can identify patterns that a human might miss. For example, an adult male asking a pre-teen girl what she wore to school can be flagged as grooming by AI and removed from the platform immediately.
Natural language processing
In recent years, there’s been a boom in AI-based content generation and marketing. However, the application of AI to online community moderation has been slower to gain traction. Using natural language processing, AI-based tools can analyze texts, visuals and videos to identify harmful behaviors. This allows brands to keep their communities safe and healthy while reducing manual moderator workloads.
A robust moderation AI tool will utilize multiple technologies, including word filters and RegEx solutions, classifiers and contextual AI. Spectrum Labs’ Guardian AI solution employs all three. This approach improves accuracy and reduces the time required for human moderators to complete tasks.
Many user-generated content sites have problems with hate speech and violent extremism. One of the biggest obstacles is language variation. For example, “leet speak,” a secret language used by hackers to discuss illegal activities on bulletin board systems, can easily slip past keyword-based AI algorithms. However, adding new languages to an existing AI system requires extensive testing and training.
Computer vision
The use of computer vision in content moderation is a powerful tool to keep online communities safe from harmful and offensive material. It uses machine learning algorithms to identify specific types of content, such as explicit nudity, suggestiveness, and violence, and identifies potential violations of community guidelines. It also helps reduce operational costs by automating the manual review process.
While generative AI tools like ChatGPT and Bard have received widespread adoption, they are limited in their ability to capture the nuances of human language. To address this, Spectrum Labs’ models are trained on a narrow domain of behavior and guided by active learning with human feedback.
Building a content moderation AI tool in house requires several types of machine learning and a team of data scientists to ensure that training data is carefully sourced, labeled, and updated regularly. In addition, a robust quality assurance cycle is required to test the model and determine whether it is functioning well.
Voice analysis
User-generated content (UGC) provides a great deal of value for communities, but it’s also a challenge to moderate. Moderation is a labor-intensive process that requires trained human moderators to keep up with the volume of content.
AI can reduce the burden of manual moderation by identifying and removing harmful or inappropriate content. This type of AI uses image processing, natural language processing, and voice analysis to detect offensive or abusive content. It can even be used to analyze live content, which is especially important for video platforms that must be able to respond quickly to abuse incidents.
The best AI content moderation tools use machine learning algorithms and large datasets to create detection tools that are accurate, scalable, and consistent. These tools also undergo regular tuning cycles that incorporate customer feedback, moderator actions, and emerging slang and connotations. This ensures that the AI models are up to date and continue to be effective at detecting prohibited content.