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<div class="" style="color:rgb(33,33,33); font-family:Calibri; font-size:16px">This is an announcement of <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Christian</span></span> <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Cianfarani</span></span>'s
MS Presentation</div>
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<div class="" style="color:rgb(33,33,33); font-family:Calibri; font-size:16px">Candidate: <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Christian</span></span> <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Cianfarani</span></span></div>
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Date: Monday, May 06, 2024</div>
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Time: 10 am CT</div>
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Location: JCL 346</div>
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Title: Differential Privacy Does Not Prevent Lawful Redistricting</div>
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Abstract: Data from the Decennial Census is published only after applying a disclosure avoidance system (DAS). Data users were shaken by the adoption of differential privacy in the 2020 DAS, a radical departure from past methods. The change raises the question
of whether redistricting law permits, forbids, or requires taking account of the effect of disclosure avoidance. It is an open legal question and the uncertainty creates legal risks, as Alabama argued in a lawsuit seeking to prevent the 2020 DAS's use.</div>
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We envision a user who seeks to proactively or defensively account for the effects of disclosure avoidance, specifically in the context of One Person One Vote and majority-minority districts. What discrepancies arise if the user does nothing to account for
disclosure avoidance? How might the user adapt her analyses to mitigate those discrepancies? We study these questions by comparing the official 2010 Redistricting Data to the 2010 Demonstration Data---created using the 2020 DAS---in an analysis of millions
of algorithmically generated state legislative redistricting plans. Doing nothing, individual plans often exceed population balance targets or overcount the number of majority-minority districts. With minor adaptations, the underlying policy goals remain achievable.
Tweaking the tolerances of the algorithms enables population balance. And the maximum number of majority-minority districts that are possible is largely unaffected, leaving intact the status quo for an important legal test in racial gerrymandering cases.</div>
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Based on these results, Alabama's claim that differential privacy ``inhibits a State's right to draw fair lines" appears unfounded.</div>
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Advisors: Aloni Cohen</div>
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Committee Members: Aloni Cohen, Aaron Schein, and Blase Ur</div>
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