Label Studio for Trust & Safety

Whether you're working on content moderation, fraud detection, or any of the things you do to protect your users and business, we know that Trust & Safety is a dynamic environment where efficiency and flexibility are paramount. Designed and laser-focused on data quality and customizability, Label Studio gives your team the tools you need to succeed.

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Adapt to changes quickly and efficiently

Difficult labeling problems often require unique context for accurate classification. Label Studio’s adaptability enables your team to be responsive and flexible in dealing with an ever-changing array of novel attacks and problems.

Multi-modal capability means you can use Label Studio for a wide variety of data types and use cases including text, images, video, time series, and more.
Flexible UI enables you to create an efficient labeling interface that is customized to your specific needs and workflow requirements.

One streamlined workflow for every team

A single platform for all use cases improves cross-team engagement and cut down on complexity and cost.

Multi-modality can support all T&S initiatives along with other internal ML initiatives. Everyone is on the same platform which enables better cross-department coordination and decreases complexity and cost.
Management and communication tools speed and enable communication between different departments, external labelers, and internal labeling teams
Customizable workflows allow flexibility in labeling and review process to ensure data quality and team efficiency

Quality data is mission-critical

Nowhere is model performance more important than in Trust and Safety. Label Studio is purpose-built to ensure that you are getting the highest quality data for your models.

Annotator agreement reports and metrics drive workflow and annotator performance improvements, and indicate when annotators come across difficult labeling items.
Real-time labeling monitoring including accuracy and consensus scores to understand how labeling efforts are progressing. This enables you to make changes while the data is being labeled - not when the model goes into production.
Use our quality dashboards to cut down the amount of time you spend evaluating dataset, model, and annotator performance. Get granular insights into the status of tasks, annotations, reviews, and labels, all in one place.

Automate labeling accurately

Label Studio's range of automation options give you leverage for your labeling workforce.

Pre-labeling allows you to integrate your model and have it make predictions that your labelers can evaluate
Active learning trains your model in real time as you label data
Automated labeling leverages generative AI to label data quickly and accurately with limited human interaction