For global platforms, fairness in AI starts with data that mirrors the world. To reduce bias and improve accuracy across languages, accents and demographics, a leading social media and tech company set out to test and strengthen the fairness of its machine learning models.
To run the test, the company needed thousands of audio and video samples from participants reading complex scripts, answering questions in their native languages and following strict technical requirements. The timeline was tight.
Company leaders brought in TaskUs to deliver. We provided end-to-end AI data services — from recruiting participants and capturing samples to validating quality and securing data.
Read the case study to find out:
- How a global recruitment strategy enabled large-scale, diverse data collection
- How our experts met strict technical and quality requirements
- How we put the proper security measures in place to protect personal and proprietary data
