Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation

Understand

Content moderation is often performed by a collaboration between humans and machine learning models.

• However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system performance.
• This work presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process.
• First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions.