#ai | Humans in the Loop

Our new article breaks down the 3 most common ways synthetic data breaks AI models, from amplifying hidden biases to triggering severe model drift and details the actionable HITL workflows your team needs to prevent these critical and costly synthetic data pitfalls.

Computer vision

3 Ways Synthetic Data Breaks Models and How Human Validators Fix Them

by Yuliia Tatarets November 18, 2025December 1, 2025

Tess Valbuena, CEO of Humans in the Loop, speaking at Forbes Bulgaria Innovation Forum 2025 about ethical AI and fair work

News

Tess Valbuena speaks at Forbes Bulgaria Innovation Forum

by Elene Peradze November 14, 2025November 17, 2025

Illustrated flowchart of human-in-the-loop video annotation process

Computer vision

How HITL Video Annotation Reduces AI Bias in Models (2025)

by Elene Peradze May 21, 2025May 22, 2025

Human-in-the-loop annotation powering AI-based driver monitoring systems in next-gen vehicles

Computer vision

How Human-in-the-Loop Annotation Improves Driver Monitoring

by Elene Peradze May 5, 2025May 5, 2025

The cost of bad data why accurate data labeling is critical for AI success

Webinar

Episode 8 – The cost of bad data

by Yuliia Tatarets March 25, 2025March 26, 2025