With laws governing online censorship differing across continents and nations, content moderation is complex. Humans are simply not fast enough to review a huge volume of content manually and prevent harmful behavior at scale.
This is where AI comes in. It is a faster, more effective and more accurate tool for detecting sensitive content such as leet speak (l337, used by hackers to avoid detection). It’s also better at picking up on patterns that humans might miss.
Improved User Experience
While human moderators can interpret nuance and context, their overwhelming workload and high expectations for productivity create perfect conditions for unconscious bias to float to the surface and affect their instinctive responses. AI-based solutions can help eliminate these cognitive stressors by identifying the most likely threats and surfacing them to human moderators closer to real-time for quicker review.
This can be done by using natural language processing (NLP) to identify threatening, offensive, or otherwise potentially harmful content in text, audio, video, or images. By combining NLP with computer vision, NLP can also help find threats in images that aren’t necessarily recognized by humans.
It’s important to note that, like any tool, AI moderation does have limitations. One of the biggest concerns is that it may be prone to bias, which could lead to discriminatory outcomes. This is why it’s important to use diverse training datasets and ensure that there are avenues for feedback from users.
Reduced Risk of Harmful Content
Aside from the ethical and financial reasons to utilize content moderation AI, it is also a safe and efficient way to manage user-generated content (UGC). It can help relieve moderators of repetitive or unpleasant tasks at various stages in the moderation process, increase safety for users and their brands, and streamline operations.
Human moderation is limited by the nuances of language and the ability to understand context. For example, a term that may be harmless in one culture can be offensive or harassing in another. This is why the ability to rely on user reports is critical in ensuring that all content is reviewed and moderated appropriately.
For instance, Spectrum Labs Guardian can use visual question answering to allow human moderators to gauge the potential harmfulness of content without viewing it. This reduces the amount of time that moderators have to spend looking at potentially damaging content and can minimize the impact on their mental health.
Artificial intelligence can evaluate content faster than human moderators, reducing the time required to identify and remove harmful submissions. This allows businesses to save operational costs associated with training and managing in-house moderation teams.
Using NLP and text classification techniques, AI-based content moderation tools can detect inappropriate language quickly and efficiently. This helps to minimize user experience issues and ensures a safe environment for communities.
Computer vision algorithms can also identify harmful images and videos, ensuring users are protected from distressing visuals. Additionally, voice-to-text transcription allows AI-based systems to analyze audio submissions for harmful language and inappropriate content.
It is important to keep in mind that AI-based tools may inevitably produce false positives or negatives, which requires human oversight and ongoing system refinements. This includes ensuring diverse training data, incorporating avenues for feedback, and establishing regular human in-the-loop tuning cycles. These iterative processes help AI models develop a better understanding of nuanced context, cultural sensitivities, and emerging language trends.
As AI technology becomes more effective at detecting harmful content, it frees up your team’s time to focus on other marketing initiatives. This allows you to scale your business according to growth targets without requiring additional manpower investments.
This approach requires pre-scanning of all UGC to eliminate the risk of introducing harmful material onto your website or social media platform in the first place. While this strategy offers a high level of security, it can also slow down new content posting and cause frustration for online community members who are accustomed to seeing their posts instantly.
Additionally, laws governing online censorship differ from continent to nation and can sometimes be confusing. Creating a robust artificial intelligence system that complies with these standards can be challenging. The best systems incorporate a human in the loop through active learning cycles. This includes customer feedback, moderator actions (e.g. de-flagging text that was wrongly flagged as profanity), and language model updates to reflect emerging slang and connotations.