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Arial, Helvetica, sans-serif;">Apologies for cross-posting</p>
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Arial, Helvetica, sans-serif;">====</p>
<p style="-webkit-tap-highlight-color: transparent; font-style:
normal; font-variant-ligatures: normal; font-variant-caps: normal;
font-weight: 400; letter-spacing: normal; orphans: 2; text-align:
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Arial, Helvetica, sans-serif;"><b
style="-webkit-tap-highlight-color: transparent;">1-day workshop
June 15, 2023<span> </span></b>in conjunction with<span> </span><b
style="-webkit-tap-highlight-color: transparent;">AIME 2023 </b>(<a class="moz-txt-link-freetext" href="https://aime23.aimedicine.info">https://aime23.aimedicine.info</a>)<span
style="-webkit-tap-highlight-color: transparent; font-size:
11pt;">, Portoroz, Slovenia </span></p>
<p style="-webkit-tap-highlight-color: transparent; font-style:
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Arial, Helvetica, sans-serif;"><a class="moz-txt-link-freetext" href="https://www.um.es/medailab/events/XAI-Healthcare/">https://www.um.es/medailab/events/XAI-Healthcare/</a><b
style="-webkit-tap-highlight-color: transparent;"><br
style="-webkit-tap-highlight-color: transparent;">
</b></p>
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Arial, Helvetica, sans-serif;"><b
style="-webkit-tap-highlight-color: transparent;">Important
dates</b><br style="-webkit-tap-highlight-color: transparent;">
<b style="-webkit-tap-highlight-color: transparent;">April 24</b>,
2023 Paper submission<br style="-webkit-tap-highlight-color:
transparent;">
<b style="-webkit-tap-highlight-color: transparent;">May 11</b>,
2023 Acceptance<br style="-webkit-tap-highlight-color:
transparent;">
<b style="-webkit-tap-highlight-color: transparent;">May 15</b>,
2023 Final mansucript<br style="-webkit-tap-highlight-color:
transparent;">
<br style="-webkit-tap-highlight-color: transparent;">
The purpose of XAI-Healthcare 2023 event is to provide a place for
intensive discussion on all aspects of eXplainable Artificial
Intelligence (XAI) in the medical and healthcare field. This
should result in cross-fertilization among research on Machine
Learning, Decision Support Systems, Natural Language,
Human-Computer Interaction, and Healthcare sciences. This meeting
will also provide attendees with an opportunity to learn more on
the progress of XAI in healthcare and to share their own
perspectives. The panel discussion will provide participants with
the insights on current developments and challenges from the
researchers working in this fast-developing field.<br
style="-webkit-tap-highlight-color: transparent;">
<br style="-webkit-tap-highlight-color: transparent;">
Explainable AI (XAI) aims to address the problem of understanding
how decisions are made by AI systems by designing formal methods
and frameworks for easing their interpretation. The impact of AI
in clinical settings and the trust placed in such systems by
clinicians have been a growing concern related to the risk of
introducing AI into the healthcare environment. XAI in healthcare
is a multidisciplinary area addressing this challenge by combining
AI technologies, cognitive modeling, healthcare science, ethical
and legal issues.<br style="-webkit-tap-highlight-color:
transparent;">
<br style="-webkit-tap-highlight-color: transparent;">
<b style="-webkit-tap-highlight-color: transparent;">Submission
Guidelines<br style="-webkit-tap-highlight-color: transparent;">
</b>Submission website:
<a class="moz-txt-link-freetext" href="https://easychair.org/conferences/?conf=xaihealthcare2023">https://easychair.org/conferences/?conf=xaihealthcare2023</a></p>
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Arial, Helvetica, sans-serif;"><span
style="-webkit-tap-highlight-color: transparent; font-size:
11pt;">All papers must be original and not simultaneously
submitted to another journal or conference. The following paper
categories are welcome:</span></p>
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Arial, Helvetica, sans-serif;"><b
style="-webkit-tap-highlight-color: transparent;">Explanation
Approaches</b>:<br style="-webkit-tap-highlight-color:
transparent;">
Model agnostic methods<br style="-webkit-tap-highlight-color:
transparent;">
Feature analysis<br style="-webkit-tap-highlight-color:
transparent;">
Visualization approaches<br style="-webkit-tap-highlight-color:
transparent;">
Example and counterfactuals based explanations<br
style="-webkit-tap-highlight-color: transparent;">
Fairness, accountability and trust<br
style="-webkit-tap-highlight-color: transparent;">
Evaluating XAI<br style="-webkit-tap-highlight-color:
transparent;">
Fairness and bias auditing<br style="-webkit-tap-highlight-color:
transparent;">
Human-AI interaction<br style="-webkit-tap-highlight-color:
transparent;">
Human-Computer Interaction (HCI) for XAI<br
style="-webkit-tap-highlight-color: transparent;">
Natural Language Processing (NLP) Explainability<br
style="-webkit-tap-highlight-color: transparent;">
</p>
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padding-bottom: 5pt; color: rgb(0, 0, 0); font-family: Verdana,
Arial, Helvetica, sans-serif;"><b
style="-webkit-tap-highlight-color: transparent;">AI techniques</b>:<br
style="-webkit-tap-highlight-color: transparent;">
Blackbox ML approaches: DL, random forest, etc.<br
style="-webkit-tap-highlight-color: transparent;">
Interpretable ML models: Rules, Trees, Bayesian networks, etc.<br
style="-webkit-tap-highlight-color: transparent;">
Statistical models and reasoning<br
style="-webkit-tap-highlight-color: transparent;">
Case-based reasoning<br style="-webkit-tap-highlight-color:
transparent;">
Natural language processing and generation<br
style="-webkit-tap-highlight-color: transparent;">
Abductive Reasoning<br style="-webkit-tap-highlight-color:
transparent;">
</p>
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style="-webkit-tap-highlight-color: transparent;">Target
healthcare problems</b>:<br style="-webkit-tap-highlight-color:
transparent;">
Infection challenges (COVID, Antibiotic Resistance, etc.)<br
style="-webkit-tap-highlight-color: transparent;">
Trustworthy AI<br style="-webkit-tap-highlight-color:
transparent;">
Chronic diseases<br style="-webkit-tap-highlight-color:
transparent;">
Ageing & home care<br style="-webkit-tap-highlight-color:
transparent;">
Diagnostic systems</p>
<p style="-webkit-tap-highlight-color: transparent; font-style:
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font-weight: 400; letter-spacing: normal; orphans: 2; text-align:
start; text-indent: 0px; text-transform: none; white-space:
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0px; background-color: rgb(255, 255, 255);
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Arial, Helvetica, sans-serif;"><b
style="-webkit-tap-highlight-color: transparent;">Organizing
Committee</b><br style="-webkit-tap-highlight-color:
transparent;">
Concha Bielza, Dept. of Artificial Intelligence, Universidad
Politécnica de Madrid<br style="-webkit-tap-highlight-color:
transparent;">
Pedro Larrañaga, Dept. of Artificial Intelligence, Universidad
Politécnica de Madrid<br style="-webkit-tap-highlight-color:
transparent;">
Primoz Kocbek, Faculty of Health Sciences, University of Maribor<br
style="-webkit-tap-highlight-color: transparent;">
Jose M. Juarez, Faculty of Computer Science, Universidad de Murcia<br
style="-webkit-tap-highlight-color: transparent;">
Gregor Stiglic, Faculty of Health Sciences, University of Maribor<br
style="-webkit-tap-highlight-color: transparent;">
Alfredo Vellido, Universitat Politècnica de Catalunya and
IDEAI-UPC</p>
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