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		<title>Startari 2025b - Revision history</title>
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		<updated>2026-05-01T08:46:40Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Startari_2025b&amp;diff=322437&amp;oldid=prev</id>
		<title>Agustinvstartari: Agustinvstartari moved page Draft Startari 612424999 to Startari 2025b</title>
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				<updated>2025-07-22T15:56:39Z</updated>
		
		<summary type="html">&lt;p&gt;Agustinvstartari moved page &lt;a href=&quot;/public/Draft_Startari_612424999&quot; class=&quot;mw-redirect&quot; title=&quot;Draft Startari 612424999&quot;&gt;Draft Startari 612424999&lt;/a&gt; to &lt;a href=&quot;/public/Startari_2025b&quot; title=&quot;Startari 2025b&quot;&gt;Startari 2025b&lt;/a&gt;&lt;/p&gt;
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				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 15:56, 22 July 2025&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan='2' style='text-align: center;' lang='en'&gt;&lt;div class=&quot;mw-diff-empty&quot;&gt;(No difference)&lt;/div&gt;
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		<author><name>Agustinvstartari</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Startari_2025b&amp;diff=322436&amp;oldid=prev</id>
		<title>Agustinvstartari: Created page with &quot; == Abstract ==  &lt;p&gt;This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transf...&quot;</title>
		<link rel="alternate" type="text/html" href="https://www.scipedia.com/wd/index.php?title=Startari_2025b&amp;diff=322436&amp;oldid=prev"/>
				<updated>2025-07-22T15:56:36Z</updated>
		
		<summary type="html">&lt;p&gt;Created page with &amp;quot; == Abstract ==  &amp;lt;p&amp;gt;This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transf...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;p&amp;gt;This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transformerbased classifiers on labeled accounting data to predict expense categories and observed that these models frequently relied on grammatical form rather than financial semantics. We extracted syntactic features including nominalization frequency, defined as the ratio of deverbal nouns to verbs; coordination depth, measured by the maximum depth of coordinated clauses; and subordination complexity, expressed as the number of embedded subordinate clauses per sentence. Using SHAP (SHapley Additive exPlanations), we identified that these structural patterns significantly contribute to false allocations, thus increasing the likelihood of audit discrepancies. For interpretability, we applied the method introduced by Lundberg and Lee in their seminal work, &amp;amp;ldquo;A Unified Approach to Interpreting Model Predictions,&amp;amp;rdquo; published in Advances in Neural Information Processing Systems 30 (2017): 4765&amp;amp;ndash;4774. To mitigate these syntactic biases, we implemented a rule-based debiasing module that reparses each narrative into a standardized fair-syntax transformation, structured around a&amp;lt;/p&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Document ==&lt;br /&gt;
&amp;lt;pdf&amp;gt;Media:Draft_Startari_612424999-6617-document.pdf&amp;lt;/pdf&amp;gt;&lt;/div&gt;</summary>
		<author><name>Agustinvstartari</name></author>	</entry>

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