  Algorithmic content moderation manages an explosive number of user-created
content shared online everyday. Despite a massive number of 3D designs that are
free to be downloaded, shared, and 3D printed by the users, detecting
sensitivity with transparency and fairness has been controversial. Although
sensitive 3D content might have a greater impact than other media due to its
possible reproducibility and replicability without restriction, prevailed
unawareness resulted in proliferation of sensitive 3D models online and a lack
of discussion on transparent and fair 3D content moderation. As the 3D content
exists as a document on the web mainly consisting of text and images, we first
study the existing algorithmic efforts based on text and images and the prior
endeavors to encompass transparency and fairness in moderation, which can also
be useful in a 3D printing domain. At the same time, we identify 3D specific
features that should be addressed to advance a 3D specialized algorithmic
moderation. As a potential solution, we suggest a human-in-the-loop pipeline
using augmented learning, powered by various stakeholders with different
backgrounds and perspectives in understanding the content. Our pipeline aims to
minimize personal biases by enabling diverse stakeholders to be vocal in
reflecting various factors to interpret the content. We add our initial
proposal for redesigning metadata of open 3D repositories, to invoke users'
responsible actions of being granted consent from the subject upon sharing
contents for free in the public spaces.
