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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">JDS</journal-id>
<journal-title-group><journal-title>Journal of Data Science</journal-title></journal-title-group>
<issn pub-type="epub">1683-8602</issn><issn pub-type="ppub">1680-743X</issn><issn-l>1680-743X</issn-l>
<publisher>
<publisher-name>School of Statistics, Renmin University of China</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">JDS1130</article-id>
<article-id pub-id-type="doi">10.6339/24-JDS1130</article-id>
<article-categories><subj-group subj-group-type="heading">
<subject>Education in Data Science</subject></subj-group></article-categories>
<title-group>
<article-title>Demonstrative Evidence and the Use of Algorithms in Jury Trials</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4145-9630</contrib-id>
<name><surname>Rogers</surname><given-names>Rachel</given-names></name><email xlink:href="mailto:rachel.rogers@huskers.unl.edu">rachel.rogers@huskers.unl.edu</email><xref ref-type="aff" rid="j_jds1130_aff_001">1</xref><xref ref-type="corresp" rid="cor1">∗</xref>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3803-0972</contrib-id>
<name><surname>VanderPlas</surname><given-names>Susan</given-names></name><xref ref-type="aff" rid="j_jds1130_aff_001">1</xref>
</contrib>
<aff id="j_jds1130_aff_001"><label>1</label>Department of Statistics, <institution>University of Nebraska-Lincoln</institution>, Lincoln, Nebraska, <country>United States of America</country></aff>
</contrib-group>
<author-notes>
<corresp id="cor1"><label>∗</label>Corresponding author. Email: <ext-link ext-link-type="uri" xlink:href="mailto:rachel.rogers@huskers.unl.edu">rachel.rogers@huskers.unl.edu</ext-link>.</corresp>
</author-notes>
<pub-date pub-type="ppub"><year>2024</year></pub-date><pub-date pub-type="epub"><day>2</day><month>5</month><year>2024</year></pub-date><volume>22</volume><issue>2</issue><fpage>314</fpage><lpage>332</lpage><supplementary-material id="S1" content-type="archive" xlink:href="jds1130_s001.zip" mimetype="application" mime-subtype="x-zip-compressed">
<caption>
<title>Supplementary Material</title>
<p>The Supplementary Material includes: (1) Statistical models and additional graphs for study questions; (2) Code for the creation of the survey app; (3) Survey data and testimony outline; (4) Source files for paper.</p>
</caption>
</supplementary-material><history><date date-type="received"><day>31</day><month>7</month><year>2023</year></date><date date-type="accepted"><day>6</day><month>4</month><year>2024</year></date></history>
<permissions><copyright-statement>2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.</copyright-statement><copyright-year>2024</copyright-year>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Open access article under the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">CC BY</ext-link> license.</license-p></license></permissions>
<abstract>
<p>We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom – where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.</p>
</abstract>
<kwd-group>
<label>Keywords</label>
<kwd>explainable machine learning</kwd>
<kwd>jury perception</kwd>
</kwd-group>
<funding-group><award-group><funding-source xlink:href="https://doi.org/10.13039/100000161">NIST</funding-source><award-id>70NANB20H019</award-id></award-group><award-group><funding-source xlink:href="https://doi.org/10.13039/100009227">Iowa State University</funding-source></award-group><funding-statement>This work was funded (or partially funded) by the Center for Statistics and Applications in Forensic Evidence (CSAFE) through Cooperative Agreements 70NANB15H176 and 70NANB20H019 between NIST and Iowa State University, which includes activities carried out at Carnegie Mellon University, Duke University, University of California Irvine, University of Virginia, West Virginia University, University of Pennsylvania, Swarthmore College and University of Nebraska, Lincoln. </funding-statement></funding-group>
</article-meta>
</front>
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