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							<persName><forename type="first">Anne</forename><surname>Liret</surname></persName>
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							<persName><forename type="first">Kyle</forename><surname>Martin</surname></persName>
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								<orgName type="department">Belén Díaz Agudo</orgName>
								<orgName type="institution">University Complutense of Madrid</orgName>
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									<country key="ES">Spain</country>
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								<orgName type="department">Bruno Fleisch (BT France)</orgName>
								<orgName type="institution">David Leake (Indiana University</orgName>
								<address>
									<country key="US">USA</country>
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								<orgName type="institution">Robert Gordon University</orgName>
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									<settlement>Aberdeen</settlement>
									<country>United Kingdom </country>
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								<orgName type="department">Juan A</orgName>
								<orgName type="institution">Recio García (University Complutense of Madrid</orgName>
								<address>
									<country key="ES">Spain</country>
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								<orgName type="institution">Anjana Wijekoon (Robert Gordon University</orgName>
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									<country key="GB">Scotland</country>
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<div xmlns="http://www.tei-c.org/ns/1.0"><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.</p><p>Different user personas can be defined by the participants, each of them with a distinctive objective or intent for explainability.</p><p>Participants will also need to provide for how the different 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></div>
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