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  <front>
    <journal-meta>
      <journal-id journal-id-type="iso-abbrev">Arch Pharm Pract</journal-id>
      <journal-id journal-id-type="publisher-id">archivepp.com</journal-id>
      <journal-id journal-id-type="publisher-id">Arch Pharm Pract</journal-id>
      <journal-title-group>
        <journal-title>Archives of Pharmacy Practice</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2320-5210</issn>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">archivepp.com-1253</article-id>
      <article-id pub-id-type="doi">10.51847/rgnlCe8U0W</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original research</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Building an Explainability Contract between Clinical Algorithms and Practicing Pharmacists</article-title>
      </title-group>
                    <contrib-group>
                      <contrib contrib-type="author">
              <name>
                <surname>Anderson</surname>
                <given-names>Mark</given-names>
              </name>
                              <xref rid="aff1" ref-type="aff">1</xref>
                                                            <xref rid="cor1" ref-type="corresp" />
                          </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Wong</surname>
                <given-names>Lisa</given-names>
              </name>
                              <xref rid="aff2" ref-type="aff">2</xref>
                                        </contrib>
                      <contrib contrib-type="author">
              <name>
                <surname>Lee</surname>
                <given-names>Sarah</given-names>
              </name>
                              <xref rid="aff1" ref-type="aff">1</xref>
                                        </contrib>
                  </contrib-group>
                  <aff id="aff1">
            <label>1</label>Department of Explainable AI in Pharmacy, College of Pharmacy, University of Florida, Gainesville, United States.
          </aff>
                  <aff id="aff2">
            <label>2</label>Department of Algorithm Transparency and Pharmacy Practice, Faculty of Pharmacy, National University of Singapore, Singapore.
          </aff>
                          <author-notes>
            <corresp id="cor1">
              <bold>Address for correspondence:</bold> Prof. Wael Abu Dayyih, Department of
              Pharmaceutical Chemistry, Faculty of Pharmacy, Mutah University, Al-Karak 61710, Jordan.
                              E-mail: <email xlink:href="mark.anderson@ufl.edu">mark.anderson@ufl.edu</email>
                          </corresp>
          </author-notes>
                    <pub-date pub-type="epub">
        <day>30</day>
        <month>06</month>
        <year>2025</year>
      </pub-date>
      <volume>16</volume>
      <issue>2</issue>
      <fpage>26</fpage>
      <lpage>34</lpage>
      <permissions>
        <copyright-statement>
          Copyright: &#x000a9; 2026 Archives of Pharmacy Practice
        </copyright-statement>
        <copyright-year>2026</copyright-year>
        <license>
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/"
            specific-use="textmining" content-type="ccbyncsalicense">
            https://creativecommons.org/licenses/by-nc-sa/4.0/</ali:license_ref>
          <license-p>This is an open access journal, and articles are distributed under the terms of
            the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows
            others to remix, tweak, and build upon the work non-commercially, as long as appropriate
            credit is given and the new creations are licensed under the identical terms.</license-p>
        </license>
      </permissions>
      <abstract>
        <title>A<sc>BSTRACT</sc></title>
        <p>Clinical algorithms increasingly support medication-risk identification, prescription prioritization, alert management, and other pharmacy activities. Yet explainability is commonly treated as a technical property of a model rather than an accountable relationship among developers, health-care organizations, practising pharmacists, and affected patients. An explanation may appear plausible while omitting uncertainty, local applicability, data limitations, workflow consequences, or mechanisms for professional challenge. This article proposes an explainability contract for pharmacist-facing clinical algorithms. The contract is a non-legal, sociotechnical governance construct that links each algorithmic role in the medication-decision lifecycle to defined parties, pharmacist rights, reciprocal duties, required disclosures, and procedures for challenge, correction, and escalation. Its principal components include intended-use boundaries, data provenance, patient-specific rationale, uncertainty and calibration, local-validity information, professional override, version traceability, and documented resolution of material concerns. The framework distinguishes model output from medication authorization, explanation plausibility from fidelity, technical performance from pharmaceutical usefulness, and implementation from demonstrated benefit. Contract performance would require staged technical, clinical, human-factors, organizational, medication-safety, and equity evaluation. Relevant outcomes include explanation fidelity, appropriate reliance, workflow accessibility, challenge responsiveness, correction completeness, subgroup performance, and recurrence of known failures. The proposed contract does not establish that explanation improves medication decisions, transfer liability to pharmacists, guarantee equitable outcomes, or constitute a validated clinical, legal, regulatory, or deployment standard. Its original contribution is to reposition explainability from optional information supplied by an algorithm into a reciprocal and contestable governance relationship requiring empirical validation.</p>
      </abstract>
      <kwd-group>
                <kwd>Digital pharmacy</kwd>
                <kwd>Artificial intelligence</kwd>
                <kwd>Pharmacy practice</kwd>
                <kwd>Medication safety</kwd>
                <kwd>Human–AI collaboration</kwd>
                <kwd>Clinical decision support</kwd>
              </kwd-group>
    </article-meta>
  </front>
</article>