From social media posts to blog comments, users generate a massive amount of user-generated content online. Using software to manage UGC can save companies time and money and ensure that the content is safe for users.
Moderation AI can help with this by flagging content that may be harmful or inappropriate. However, it is important to understand that AI can be tripped up by subjective and nuanced concepts.
What is AI-based content moderation
Every minute, 240,000 images are shared on Facebook, 65,000 photos are uploaded to Instagram, and 575,000 tweets are posted on Twitter. With such an overwhelming volume of user-generated content (UGC), it can be challenging for online communities to manage and moderate this content. Fortunately, AI-based content moderation can help you optimize your UGC management process by automating the identification and deletion of harmful content.
AI-based content moderation uses machine learning models to automatically detect, flag, and remove harmful content from your website or app. It is a powerful tool that can significantly improve your UGC management processes by reducing response times, increasing accuracy, and lowering costs.
There are many different types of AI-based content moderation technologies available, including text moderation, image moderation, and voice recording moderation. Each of these AI-based content moderation technologies has its own unique set of capabilities that can be leveraged to meet the specific needs of your business.
For example, text moderation AI uses natural language processing and machine learning to classify various forms of written content into categories such as positive, negative, or toxic. Image moderation AI uses computer vision and machine learning to detect images that are offensive or inappropriate. Voice recording moderation AI converts audio content into text and uses natural language processing to identify and classify the content.
Regardless of the type of AI-based content moderation technology used, all AI-based content moderation solutions require training to get up and running. To train the AI-based content moderation solution, you must provide it with examples of both positive and negative content so that the system can learn to recognize good or bad content. Once the AI-based content moderation solution is trained, it can then be used to automatically classify new or existing UGC.
How does AI-based content moderation work
AI is a great tool for identifying inappropriate content, but it cannot fully replace human moderators. While humans are good at catching certain categories of harmful content (like hate speech, spam, or terrorist propaganda), they can’t keep up with the volume of UGC created every day. This puts a strain on human moderation teams and can degrade the quality of their decisions over time.
As a result, it’s important for businesses to combine AI and human moderators in their content moderation strategy. AI can help automate the majority of the work and speed up the process, while human moderators can focus on catching the more complex or subjective cases.
The content moderation process begins when users upload text, images, or video to a website or platform. Then, AI algorithms use natural language processing, computer vision, and other machine learning techniques to analyze the content and determine if it’s appropriate or not.
For example, natural language processing algorithms can help identify the intended meaning of text and decipher emotions like anger, bullying, or sarcasm. While computer vision can detect the presence of certain objects in images, such as nipples or violence.
Once the AI has analyzed the content, it will make an adjudication decision and take action accordingly (like blocking or reporting). This can be done automatically, or the results can be sent to human moderators for further review. In either case, the end goal is to ensure that all content on a platform is safe for its audience and in line with the company’s policies. This process is particularly challenging when it comes to global content, as laws governing online censorship vary widely between continents and nations.
Why is AI-based content moderation important
As the scale and speed of online content production increases, it becomes impossible for humans to keep up. This is where AI comes in. Moderation AI can identify harmful content across text, images, GIFs, audio and live streams. It’s able to detect standard categories like Hate speech, Sexual content and bullying as well as more advanced classifiers tailored to your platform’s rules.
While AI does not replace human moderators, it can help them do their work faster and more accurately. This is especially important when working on a global scale where laws and perceptions of what is ‘harmful’ or ‘illicit’ differ between continents and nations.
Using an automated approach is also a lot cheaper than hiring human moderators to do the same job. AI can process vast volumes of UGC in a fraction of the time, allowing businesses to manage their sites and services more effectively.
It’s crucial to keep an eye on performance and monitor for signs of data drift. One way of doing this is by setting up a monitoring loop that looks at the difference between the accuracy of the model and the percentage of content it auto-curates above a certain threshold of confidence. This metric is called coverage and it can be used to quickly pinpoint if the model is deviating from its expected performance.
Another measure is intercoder reliability. This is the consistency with which different labelers (people who annotate a dataset) agree on whether the same sample should be classified as a particular type of content. It’s a good indicator of how stable the model is and, if there has been a significant drop in accuracy, it may be necessary to retrain the AI with recent content that was vetted by human moderators.
What are the benefits of AI-based content moderation
Using AI for moderation can save human moderators time and effort by sorting out questionable content for their review. It can also make them more productive by channeling content that may require greater scrutiny, like images of nipples, to moderators who specialize in that type of image and can do the work faster.
AI is also much more scalable than human moderators, as it can look through large quantities of user-generated content quickly and consistently. This can help you avoid costly mistakes and reputational damage that can be caused by not properly moderating content.
One of the biggest benefits of AI is that it can prevent human biases and personal interpretations from affecting moderation decisions. This can result in more consistent and equitable judgments compared to human moderators.
Additionally, AI can identify a variety of different types of inappropriate content, including spam, bullying, terrorist propaganda, and more. This can significantly reduce the amount of time your moderators spend going through this content and frees up their time to focus on more nuanced issues, such as cultural allusions, or identifying humor in text.
Once your AI model has been trained and deployed, it should be continuously monitored and improved. Specifically, you should be looking at its accuracy (how often it is correct) and coverage (what percentage of the UGC it automatically curates). If these metrics begin to fall below your desired targets, it may indicate that your AI model needs to be re-trained or that your business costs are increasing too much. This is when it is a good idea to consider adjusting your confidence threshold, which will result in a decrease in the number of items that are auto-curated but may impact your accuracy in the short term.
Are there any limitations to AI-based content moderation
Although AI-based content moderation is growing in popularity, it still has some limitations. The first is that it can be difficult to identify subtle infractions. For example, a piece of text may be considered hate speech in one context but not in another. The same can be said for images.
Additionally, AI can be prone to biases when it comes to filtering large amounts of data. This can be mitigated by leveraging multiple sources of information when making decisions. One way to do this is by using a majority voting system. Another is by monitoring the fraction of human moderators who agree with a decision. This metric can be used to measure how effective an AI system is at content moderation.
In addition, AI-based content moderation isn’t always able to understand the nuances of language and cultural allusions. This can be a challenge when trying to enforce rules that are universally applicable. For example, slang terms that are acceptable in one culture might be considered offensive in another.
As a result, AI-based content moderation can be limited in its ability to reduce harmful content. However, by combining different types of AI technologies, such as computer vision and natural language processing, it is possible to create more comprehensive tools that can better detect unwanted or harmful content.
For example, computer vision can be used to scan images and videos for potentially harmful content. This can be combined with natural language processing to evaluate the content’s tone and intent. This can help to reduce the amount of harmful content that human moderators must view directly. In addition, AI can also be used to blur certain elements of an image or video to limit its exposure to the public.