3 AI data-labeling tools that pair perfectly with Prolific

Data labeling is a crucial step in training AI models.

It’s also something we don’t offer ourselves. And for good reason.

Prolific is purely a participant-sourcing tool. We focus on providing the best people for the task. This gives you more flexibility to customize your workflow and choose the best solution for your specific study.

But integrating labeling tools with our platform is easy and seamless. And with the right integration, you can significantly speed up how you collect and annotate data.

Here are a few free, open-source picks we’d recommend – and the research you can use them for.

1. Potato

What is it?

First up, we have the portable text annotation tool Potato (get it?). This easy-to-use, web-based data annotation tool is perfect for tasks like:

• Text classification
• Sequence labeling
• Data-to-text annotation

Potato allows you to quickly mock up and deploy a variety of text annotation tasks. It works in the backend as a web server that you can launch locally – and then annotators can use the web-based front-end to work through data.

Why use it?

With its editable, user-friendly interface, you can quickly annotate data and create high-quality datasets. It takes very little training and no coding skills.

Other benefits include:

Simple, flexible setup

Potato offers over 10 templates and schemas – including radio, Likert, checkbox, textbox, span, pairwise comparison, best-worst-scaling, and image/video-as-label – plus multilingual and multi-task annotation. It’s also fit for both internal annotation and crowdsourcing.

Excellent quality control

The tool has a built-in attention test, qualification test, and time tracker, as well as pre- and post-screening questions. So, you can identify potential spammers, analyze annotator behaviors, and – most importantly – uphold data quality.

Improved productivity

With Potato, you can enjoy active learning, dynamic highlighting (automatically showing potential associations between labels and keywords), hover-over label tooltips, and keyboard shortcut input. What’s more, it allows for the easy sharing of annotation logs and configurations.

Who’s it for?

If you’re working on natural language processing tasks, Potato is ideal. For instance, you can use it to label text for sentiment analysis, annotate named entities in text, or create structured summaries from data.

2. Mephisto

What is it?

Mephisto is another platform for collecting and labeling data. It’s built for big and complex annotation tasks.

Developed by the Meta AI team, it provides an ecosystem where you create, manage, and deploy tasks while keeping full control over your data.

Why use it?

Researchers can swap strategies for collecting data to use again and iterate on. You can also change out components and quickly find the exact annotations you need, which makes custom task creation easier.

Some more benefits are:

Plug-and-play abstractions

You can use the same code to collect data across different domains – such as research topics, crowdsourcing, and server configurations. Mephisto also includes workflow guides for the entire process, from concept to completion.

Local pre-pilot testing and iterating

The tool provides a simple method for launching small pilot batches and viewing the results over many employees once the task is complete. This makes it easy to spot any flaws with the task, or workers submitting invalid data.

Privacy protection

Mephisto contains key privacy protection methods, like masking worker identity, and its creators plan to add more capabilities in the future. This’ll include reporting worker statistics on contributions to a data set, and highlighting projects that expressly try to de-bias data sets. The platform also lets you store and manage data locally or on a cloud server, depending on your preferences and security requirements.

Who’s it for?

Mephisto is great if you need a data labeling solution that you can scale and customize. It’s ideal for complex tasks such as dialogue systems, semantic role labeling, and data collection for reinforcement learning.

3. OpenLabeler

What is it?

OpenLabeler is a tool with a focus on simplicity. It supports image annotation for object detection and image classification tasks.

Why use it?

Its plus points are:

Lightweight nature

OpenLabeler has a simple interface, making it easy to learn and use.

Cross-platform compatibility

The tool is written in Java and can run on multiple platforms, such as Windows, macOS, and Linux.

Extensible functionality

OpenLabeler's source code is available on GitHub, so developers can extend its functionality and tailor it to their specific needs.

Who’s it for?

If you’re working on computer vision tasks, OpenLabeler is for you. You can use it for object detection, image classification, or other AI tasks that require image annotation.

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The right data labeling tool can go a long way to improving the quality of your datasets and, therefore, the accuracy of your AI models.

Combine it with the Prolific platform, and you’ve got yourself a powerful toolset to collect high-quality human annotations.

Sign up today, run a pilot and see for yourself.

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