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<p style="margin:0in"><span style="color:black">It’s been interesting to see what everyone’s been working on lately. We wanted to announce a new project we will be debuting at Science Gallery Detroit as part of the Tracked and Traced exhibition opening early
this September [1].</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p style="margin:0in;background:white"><span style="color:#333333">The project,<i> Boogaloo Bias,</i> uses absurdity to reveal some of the problematic uses of facial recognition software by law enforcement, such as the practice of ‘brute forcing’ where, in
the absence of high-quality images of a suspect, agents substitute hand drawn forensic sketches, proxy images generated
</span><span style="color:black">from artist sketches</span><span style="color:#333333">, and images of celebrities the suspect is reported to resemble. In
<i>Boogaloo Bias</i>, a facial recognition algorithm searches for members of the anti-law enforcement militia the Boogaloo Bois by surveilling protest footage and live video feeds using a facial recognition algorithm trained on faces from the campy B-movie
sequel<i> Breakin’ 2: Electric Boogaloo</i>, which is also the namesake for the group. All matches made by the system will be false positives.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p style="margin:0in;background:white"><span style="color:#333333">A study by the Georgetown Law Center on Privacy and Technology found that because “[t]here are no rules when it comes to what images police can submit to face recognition algorithms to generate
investigative leads,” agents have been known to substitute not only low-quality images from CCTVs, but hand-drawn forensic sketches, proxy images generated from artist sketches, and images of celebrities thought to resemble a suspect [2]. As one might suspect,
this has resulted in numerous false-positives. Through a juxtaposition of real video footage and constructed movie characters,
<i>Boogaloo Bias</i> reveals some of the problems inherent in how facial recognition and algorithmic classification technologies are used by law enforcement.</span><o:p></o:p></p>
<p style="margin:0in;background:white"><span style="color:black"> </span><o:p></o:p></p>
<p style="margin:0in;background:white"><span style="color:#333333">The use of <i>
Breakin’ 2: Electric Boogaloo</i> is a reference to the Boogaloo Bois, an anti-law enforcement militia that derives their name from the 1984 film. The group emerged from 4chan meme culture and, since January 2020, have been present at right and left-wing protests
against coronavirus lockdowns and police brutality. In the project, a facial recognition algorithm trained on faces from
<i>Breakin’ 2</i> searches for those characters in a live video feed and videos of protest footage in the United States from 2020 and 2021. When a match is found, the face in the video is identified by a box displaying the name of the character, an accuracy
score, and a still image of the character is shown to the right of the video feed. All matches produced by our system are false-positives. The gallery installation will feature videos of protest footage and a live-feed surveillance camera for viewers to interact
with the <i>Boogaloo Bias </i>facial recognition system. An online app that will allow visitors to the project website to use their webcam to interact with the program is also in development and will be available this summer.</span><o:p></o:p></p>
<p style="margin:0in;background:white"><span style="color:black"> </span><o:p></o:p></p>
<p style="margin:0in;background:white"><span style="color:#333333">Links:</span><o:p></o:p></p>
<p style="margin:0in;background:white"><span style="color:#333333">[1] Science Gallery Detroit:
</span><span style="color:black"><a href="https://detroit.sciencegallery.com/"><span style="color:#1155CC">https://detroit.sciencegallery.com/</span></a></span><span style="color:#333333"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;color:#333333">[2] Angelyn, Clare. “Garbage In. Garbage Out. Face Recognition on Flawed Data.” Georgetown Law Center on Privacy and Technology, May 16, 2019.
</span><span style="font-size:11.0pt"><a href="https://www.flawedfacedata.com/"><span style="color:#1155CC">https://www.flawedfacedata.com/</span></a><span style="color:#333333">
</span><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Our current bios are available on our personal websites:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><a href="https://derekcurry.com/">https://derekcurry.com/</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><a href="http://jennifergradecki.com/">http://jennifergradecki.com/</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Please let us know if you have any questions about the project!<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">All our best,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Derek & Jennifer<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">-- <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Derek Curry, PhD.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt">Assistant Professor Art + Design<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt">Office: 211 Lake Hall<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt"><a href="http://derekcurry.com/"><span style="color:#0563C1">http://derekcurry.com/</span></a>
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
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