  Hate speech moderation remains a challenging task for social media platforms.
Human-AI collaborative systems offer the potential to combine the strengths of
humans' reliability and the scalability of machine learning to tackle this
issue effectively. While methods for task handover in human-AI collaboration
exist that consider the costs of incorrect predictions, insufficient attention
has been paid to accurately estimating these costs. In this work, we propose a
value-sensitive rejection mechanism that automatically rejects machine
decisions for human moderation based on users' value perceptions regarding
machine decisions. We conduct a crowdsourced survey study with 160 participants
to evaluate their perception of correct and incorrect machine decisions in the
domain of hate speech detection, as well as occurrences where the system
rejects making a prediction. Here, we introduce Magnitude Estimation, an
unbounded scale, as the preferred method for measuring user (dis)agreement with
machine decisions. Our results show that Magnitude Estimation can provide a
reliable measurement of participants' perception of machine decisions. By
integrating user-perceived value into human-AI collaboration, we further show
that it can guide us in 1) determining when to accept or reject machine
decisions to obtain the optimal total value a model can deliver and 2)
selecting better classification models as compared to the more widely used
target of model accuracy.

Human-Computer Interaction