Automated Copyright Filtering Removes Public Domain Mueller Report From Platform

* Automated upload filters are ripe for potential abuse, or
at least significant confusion.

Rightholders often raise concerns that the digital era creates
outsized problems for piracy, given how quickly information can be
circulated and spread. As a result, many have proposed solutions
such as “notice and staydown” (instead of “notice and takedown”) or
advocate for automatic copyright filters (a proposal that
ultimately was included in the EU’s Copyright Directive).

While it is easy to sympathize with rightholders’ concerns
regarding actual piracy, copyright upload filters — where a
platform filters out content before it’s even uploaded to the site
— pose their own problems and can result in the removal or
prevention of the uploading of completely non-infringing material.
User-generated content and public domain works are particularly
vulnerable. In short, automated copyright filters are not a perfect
solution.

One of the obvious potential issues with automated filters is
that it is difficult, if not impossible, for an algorithm to take
into account fair use. In the infamous “Dancing Baby” case, a
mother uploaded a video of her toddler dancing to a clip of a
Prince song (which had poor audio). The video was subjected to a
takedown notice and courts ultimately determined that a rightholder
has an obligation to consider fair use before issuing a takedown
notice. While that’s all good and well in terms of sending takedown
notices, how does an algorithm take into account fair use in
automated filtering? While there are various approaches, such as
determining what portion of content is used in the material being
uploaded, they would still be problematic for a number of reasons.
Because fair use is determined on a case-by-case basis, algorithms
cannot properly make these decisions.

Automated upload filters are also ripe for potential abuse, or
at least significant confusion. Automated filters depend on a
database of copyrighted content against which uploaded content is
matched. If there is a match between the copyrighted content in the
database and the material someone is trying to upload, it will get
flagged and automatically filtered out. These databases are
populated by content contributed by rightholders, which means that
submissions could include material not under copyright protection
or not owned by those uploading the content.

One of the best examples of material that was mistakenly flagged
for copyright infringement occurred when NASA could not upload its
own footage of the Mars lander to YouTube back in 2012. That’s
right, NASA could not upload its own video, which it created and
for which no one owns the copyright in the United States (since it
is a United States government work, it is in the public domain).
How could this possibly happen? Well, NASA’s footage was used in
various news broadcasts. The broadcast of the news was submitted to
YouTube’s content ID database by newscasters. Because the NASA
footage had been incorporated, it was flagged as a copyrighted
work. While NASA is a prominent example, certainly others could
face the same problems — such as when news programs run clips taken
by bystanders for newsworthy events or other content, and the
actual copyright owner of the underlying material may have their
own videos flagged.

More recently, automated filters also ensnared the Mueller
report, another U.S. government — and therefore public domain —
work. Some users uploaded the Mueller report, which is freely
available in a number of locations online, to Scribd. Despite its
public domain status, Scribd started mass removal of the various
uploads of the Mueller report. Again, one wonders how this result
could possibly happen, but databases that simply depend on
rightholder contributions basically operate on an honor system. In
this case, publishers have started selling bound copies of the
Mueller report (as they did for the 9/11 Commission report, which
shot up to the bestsellers list on Day 1, though I have yet to meet
anyone who actually read the entire report). These publishers
submitted a copy of the Mueller report to Scribd’s content ID
database, despite not owning any underlying copyright in the
work.

The examples of the removal of NASA’s footage or the Mueller
report are clear examples where content ID failed, due to the fact
that these works are in the public domain and free to all in the
United States. These examples don’t even cover the trickier
questions of fair use, where copyrighted content (such as a song, a
poster, or a TV show) may appear in the background of what is
actually being featured in a user-generated video. It doesn’t cover
examples of satire or political commentary, which may incorporate a
copyrighted work and thus cause a flag in the content ID
system.

As there is a growing clamor for automated filtering through
content ID systems —particularly with the passage of the EU
Directive earlier this year — the United States should tread
carefully before imposing upload filters on platforms. The
potential for abuse or mistaken matches is clear, and unintended
consequences must be taken into account.

Culled from abovethelaw.com

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