<?xml version='1.0' encoding='utf-8'?>
<?xml-stylesheet type="text/xsl" href="/v2/static/oai2.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-10-14T14:55:39Z</responseDate>
  <request identifier="oai:figshare.com:article/33814527" metadataPrefix="oai_dc" verb="GetRecord">https://api.figshare.com/v2/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:figshare.com:article/33814527</identifier>
        <datestamp>2026-09-15T17:58:51Z</datestamp>
        <setSpec>category_146</setSpec>
        <setSpec>category_734</setSpec>
        <setSpec>category_811</setSpec>
        <setSpec>category_106</setSpec>
        <setSpec>portal_5</setSpec>
        <setSpec>item_type_3</setSpec>
        <setSpec>month_year_09_2026</setSpec>
      </header>
      <metadata>
        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>&lt;p&gt;Illustrative conceptual analysis showing the expected influence of the hybrid weighting parameter (α) on explanation fidelity and human trust. Values are representative and are provided to illustrate the expected behavior of the proposed framework rather than experimentally measured results.&lt;/p&gt;</dc:title>
          <dc:creator>Arul Selvam P. (24950178)</dc:creator>
          <dc:creator>Tamije Selvy P. (24950181)</dc:creator>
          <dc:subject>Space Science</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Mathematical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>positioning hybrid xai</dc:subject>
          <dc:subject>outperform existing baselines</dc:subject>
          <dc:subject>deletion metrics across</dc:subject>
          <dc:subject>consistent evaluation protocol</dc:subject>
          <dc:subject>aligning methodological rigor</dc:subject>
          <dc:subject>additional computational overhead</dc:subject>
          <dc:subject>15 &amp;# 8211</dc:subject>
          <dc:subject>unlike prior single</dc:subject>
          <dc:subject>intuitive reasoning paths</dc:subject>
          <dc:subject>aware design indicating</dc:subject>
          <dc:subject>hybrid explainability framework</dc:subject>
          <dc:subject>framework provides explanations</dc:subject>
          <dc:subject>benchmark datasets using</dc:subject>
          <dc:subject>benchmark datasets</dc:subject>
          <dc:subject>single techniques</dc:subject>
          <dc:subject>causal reasoning</dc:subject>
          <dc:subject>aware pipeline</dc:subject>
          <dc:subject>proposed framework</dc:subject>
          <dc:subject>trust ratings</dc:subject>
          <dc:subject>technically faithful</dc:subject>
          <dc:subject>systematically evaluated</dc:subject>
          <dc:subject>study advances</dc:subject>
          <dc:subject>specific corpora</dc:subject>
          <dc:subject>sentiment analysis</dc:subject>
          <dc:subject>results show</dc:subject>
          <dc:subject>representative architectures</dc:subject>
          <dc:subject>remain opaque</dc:subject>
          <dc:subject>regulatory demands</dc:subject>
          <dc:subject>question answering</dc:subject>
          <dc:subject>practical applications</dc:subject>
          <dc:subject>potential suitability</dc:subject>
          <dc:subject>paper introduces</dc:subject>
          <dc:subject>method pipeline</dc:subject>
          <dc:subject>method approaches</dc:subject>
          <dc:subject>main contributions</dc:subject>
          <dc:subject>llama ).</dc:subject>
          <dc:subject>limiting adoption</dc:subject>
          <dc:subject>introducing less</dc:subject>
          <dc:subject>intelligent systems</dc:subject>
          <dc:subject>integrates saliency</dc:subject>
          <dc:subject>explainable ai</dc:subject>
          <dc:subject>ensuring objective</dc:subject>
          <dc:subject>demand accountability</dc:subject>
          <dc:subject>comparable assessment</dc:subject>
          <dc:subject>centered visualization</dc:subject>
          <dc:subject>case studies</dc:subject>
          <dc:subject>bridging trust</dc:subject>
          <dc:subject>based methods</dc:subject>
          <dc:subject>based attribution</dc:subject>
          <dc:subject>achieve state</dc:subject>
          <dc:description>&lt;p&gt;Illustrative conceptual analysis showing the expected influence of the hybrid weighting parameter (α) on explanation fidelity and human trust. Values are representative and are provided to illustrate the expected behavior of the proposed framework rather than experimentally measured results.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-15T17:58:37Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pone.0343472.t005</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/_p_Illustrative_conceptual_analysis_showing_the_expected_influence_of_the_hybrid_weighting_parameter_on_explanation_fidelity_and_human_trust_Values_are_representative_and_are_provided_to_illustrate_the_expected_behavior_of_the_proposed_fram/33814527</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
        </oai_dc:dc>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
