<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Organizers:</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Belén Díaz Agudo (University Complutense of Madrid, Spain) Bruno Fleisch (BT France) David Leake (Indiana University, USA) Anne Liret (BT France) Kyle Martin (Robert Gordon University</institution>
          ,
          <addr-line>Aberdeen</addr-line>
          <institution>, United Kingdom ) Juan A. Recio García (University Complutense of Madrid, Spain) Anjana Wijekoon, Robert Gordon University</institution>
          ,
          <addr-line>Scotland</addr-line>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>The objective of this challenge is to highlight your expertise, skills and experience in applying Explainable AI (XAI) techniques to a “black-box” AI model, so that the predictions of the model can be understood, improved or challenged by the users interacting with it or who are impacted by the model. Diferent user personas can be defined by the participants, each of them with a distinctive objective or intent for explainability. Participants will also need to provide for how the diferent explanations can be evaluated by these personas and conduct an evaluation of their explanation strategies. These evaluations can be automated or assessed manually by a survey performed on a users group.</p>
      </abstract>
    </article-meta>
  </front>
  <body />
  <back>
    <ref-list />
  </back>
</article>