Despite their popularity, off-the-shelf automated tools for content moderation have some significant limitations. These tools are prone to errors and often overlook harmful content in favor of flagging benign content.
Customizable AI allows Trust & Safety teams to identify and remove harmful content before it’s ever posted to communities. This includes using Natural Language Processing algorithms to decipher intended meaning, text classification to categorize text, and entity recognition to extract names and locations.
Machine learning helps reduce the burden on human moderation teams who would otherwise have to manually review thousands of images and texts. It filters the content and analyzes it against a behavior model to determine if a given piece of user-generated content is toxic or not.
The use of this type of AI in content moderation requires a robust team to build and train the algorithms. This includes a large group of data scientists who ensure that the model’s training data is labeled properly so that it can accurately predict whether behaviors violate community standards or are healthy and positive.
A key element of machine learning in content moderation is meticulous natural language processing, or the ability for machines to understand the context behind written human speech. This includes understanding subtleties of tone, like sarcasm or anger, and the ability to read emotion into a text. Additionally, NLP tools must be trained to reflect the cultural and linguistic diversity of users across the globe to prevent biases in their decision-making.
Natural Language Processing
With user-generated content (UGC) dominating the digital world, every business that utilizes UGC needs a system to monitor and surface behaviors that violate community guidelines. This includes social media platforms, e-commerce sites and hotel booking apps. These automated tools leverage various types of ML to scan images, text and video for harmful behavior. They’re also tasked with achieving high operational precision, which measures the tool’s ability to correctly identify and flag harmful content while minimizing false positives.
To help achieve high operational precision, many ML tools leverage natural language processing to understand the intended meaning behind text and decipher emotions. This helps them spot a range of malicious behaviors such as harassment, bullying and terrorism. However, it isn’t a panacea and is limited in its understanding of human context. For example, words like “stupid” can be toxic in one context but harmless in another. This makes it essential for platforms to work with a partner that can provide contextual analysis.
Computer vision is used to analyze images and determine whether they breach community guidelines. For example, it can identify nudity, weaponry and logos in images. It also works to detect if an image has been altered using deepfake technology.
Human moderation teams are overwhelmed and under pressure to deliver productivity, which can expose them to cognitive stressors that trigger unconscious bias. It’s therefore important for platforms to use AI for content moderation as a way of automating the process to reduce the risk of human exposure to harmful content.
Developing an effective content moderation AI strategy requires multiple types of machine learning models and extensive training data to ensure they accurately predict which behaviors violate community standards. It’s also important to consider the operational precision of the system, which is determined by how often it correctly identifies harmful behavior and doesn’t mistakenly flag benign content. The higher the operational precision, the more accurate your tool will be.
User-generated content benefits communities, but it can also be harmful if left unmoderated. Moderation is a time-consuming and resource-demanding task that requires scalable solutions.
The good news is that ML-based tools can keep pace with the volume of content. They can improve accuracy through active learning, and ensure that human moderators focus on the most important issues.
Image content moderation AI can detect and flag harmful images, based on a list of rules. This includes explicit nudity, suggestiveness, violence, and other categories that are against community standards.
Text and image analysis can be automated with a combination of machine learning, natural language processing, and computer vision technologies. The model can then evaluate each piece of content for harmfulness and flag it for manual review if necessary. Spectrum Labs uses a large network of vetted native language experts to label training data sets and to provide feedback during regular quality assurance cycles. This allows Spectrum Labs to create high-performing, accurate models.