  Though detection systems have been developed to identify obscene content such
as pornography and violence, artificial intelligence is simply not good enough
to fully automate this task yet. Due to the need for manual verification,
social media companies may hire internal reviewers, contract specialized
workers from third parties, or outsource to online labor markets for the
purpose of commercial content moderation. These content moderators are often
fully exposed to extreme content and may suffer lasting psychological and
emotional damage. In this work, we aim to alleviate this problem by
investigating the following question: How can we reveal the minimum amount of
information to a human reviewer such that an objectionable image can still be
correctly identified? We design and conduct experiments in which blurred
graphic and non-graphic images are filtered by human moderators on Amazon
Mechanical Turk (AMT). We observe how obfuscation affects the moderation
experience with respect to image classification accuracy, interface usability,
and worker emotional well-being.
