Content moderation is the process of regulating user-generated content to ensure it follows your platform’s guidelines. This is a vital function for all online communities.
AI-powered tools can reduce the burden on Trust & Safety teams by identifying harmful behaviors and automatically deleting them. These tools fall into three broad categories: lookups, classifiers, and contextual AI.
Pre-moderation
Every day, people add more information to the internet. Some of this information can be harmful, and businesses use content moderation to keep users safe. This process involves reviewing user-generated content (UGC) before it can appear on a website or social media page. It is an efficient way to protect your community from harmful content and can save you time.
However, the sheer volume of UGC requires fast and accurate AI-powered tools to detect offensive or toxic content. These tools must be able to recognize dozens of languages and their social contexts, and understand cultural nuances like humor or sarcasm.
Additionally, they must be able to identify and flag content that violates community guidelines or company policies. It is critical that these tools be designed and deployed in accordance with international human rights law. This ensures that they do not infringe on the privacy of users and that they can be trusted to make fair evaluations without bias.
Post-moderation
Using AI to identify inappropriate content and remove it before users see it demonstrates your company’s dedication to providing a safe online community for everyone. This proactive approach minimizes the risk of negative publicity and backlash from harmful content submissions and builds trust in your brand.
Post-moderation uses a variety of algorithms to identify inappropriate content, including text classification, sentiment analysis, and entity recognition. Text classification uses natural language processing to detect specific sentiments like anger, sarcasm, and negativity, while sentiment analysis uses neural networks to identify emotions. Entity recognition uses computer vision algorithms to scan images for recognizable objects like people, cars, or locations.
A post-moderation strategy requires human moderators to review and approve the contents of a site before it is published. The process involves monitoring the user-generated content and flagging any potentially harmful submissions. After reviewing the flagged content, a human or content moderation AI solution will decide whether it should be deleted.
Reputation management
While AI content moderation may seem like the answer to a growing problem, it has its own set of complications. The most serious issue is the lack of transparency associated with automated algorithms. They are often referred to as black boxes, and there is little known about how they are coded or what datasets they use to find correlations. This makes it difficult for researchers to determine how reliable and trustworthy the systems are.
Another issue is the risk of censorship. If the system flags an article that may be offensive or harmful, a moderator must review it and decide whether to publish it or not. This can affect user experience and create legal risks.
Another common moderation method involves community members using a rating system to report content that may be against the community’s rules. This is commonly used in tight-knit online communities. However, it can also be dangerous if used by bad actors. Spectrum Labs, an ML company, has developed the first-of-its-kind moderation tool that can protect Generative AI content from malicious behavior.
Human interaction
In addition to reducing the time required for moderation, AI can also improve transparency and accountability. This is especially important when it comes to sensitive topics like race, gender, and religion, which require a greater level of understanding and expertise than other content.
However, incorporating AI into content moderation can be expensive. For example, ML models must be updated with new data and language to avoid biases. In addition, ML models are often trained with labeled data, which is often provided by a third-party. This can lead to an increase in payroll expenses.
Automated tools can also have significant negative effects on users, especially marginalized populations. For example, one algorithm incorrectly flagged anti-trans vitriol as “toxic.” It is important for businesses to understand the limitations of AI and consider these risks when choosing an AI content moderation solution. This is particularly true for companies that are implementing a fully automated AI solution.