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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v8i1.5991</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Crossing the Achilles Heel of Algorithms: Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making</title><url>https://artdesignp.com/journal/JERA/8/1/10.26689/jera.v8i1.5991</url><author>ChenKexin</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>1</issue><history><date date-type="pub"><published-time>2024-01-23</published-time></date></history><abstract>In the developmental dilemma of artificial intelligence (AI)-assisted judicial decision-making, the technicalarchitecture of AI determines its inherent lack of transparency and interpretability, which is challenging to fundamentallyimprove. This can be considered a true challenge in the realm of AI-assisted judicial decision-making. By examining thecourt’s acceptance, integration, and trade-offs of AI technology embedded in the judicial field, the exploration of potentialconflicts, interactions, and even mutual shaping between the two will not only reshape their conceptual connotations andintellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of thejudicial trial system.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Liu H-W, Lin C-F, Chen Y-J, 2019, Beyond State v. Loomis: Artificial Intelligence, Government Algorithmization, and Accountability. International Journal of Law and Information Technology, 27(2): 122–141.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Kroll JA, Huey J, Barocas S, et al., 2017, Accountable Algorithms. University of Pennsylvania Law Review, 165(3): 633–705.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Contini F, 2020, Artificial Intelligence and the Transformation of Humans, Law and Technology Interactions in Judicial Proceedings. Law, Technology and Humans, 2(1): 4-18. https://doi.org/10.5204/lthj.v2i1.1478</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Schönberger D, 2019, Artificial Intelligence in Healthcare: A Critical Analysis of the Legal and Ethical Implications. International Journal of Law and Information Technology, 27(2): 171–203. https://doi.org/10.1093/ijlit/eaz004</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>McGregor L, Murray D, Ng V, 2019, International Human Rights Law as a Framework for Algorithmic Accountability. International and Comparative Law Quarterly, 68(2): 309 – 343. https://doi.org/10.1017/S0020589319000046</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Ananny M, Crawford K, 2016, Seeing Without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability. New Media &amp; Society, 20(3): 973–989. https://doi.org/10.1177/1461444816676645</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Gleicher M, 2016, A Framework for Considering Comprehensibility in Modeling. Big Data, 4(2): 75–88. http://doi.org/10.1089/big.2016.0007</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
