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        <article-title>Case-Based Reasoning for the Explanation of Intelligent Systems</article-title>
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          <string-name>Organizers:</string-name>
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          <institution>Juan A. Recio García (University Complutense of Madrid, Spain) Belén Díaz Agudo (University Complutense of Madrid, Spain) Anjana Wijekoon, Robert Gordon University</institution>
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          <addr-line>Scotland</addr-line>
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      <abstract>
        <p>XCBR is a workshop aiming to provide a medium of exchange for information about trends, research issues and practical experiences in the use of Case-Based Reasoning (CBR) methods for the inclusion of explanations to several AI techniques (including CBR itself). The success of intelligent systems has led to an explosion of the generation of new autonomous systems with new capabilities like perception, reasoning, decision support and self-action. Despite the tremendous benefits of these systems, they work as black-box systems and their efectiveness is limited by their inability to explain their decisions and actions to human users. The problem of explainability in Artificial Intelligence is not new but the rise of the autonomous intelligent systems has created the necessity to understand how these intelligent systems achieve a solution, make a prediction or a recommendation or reason to support a decision in order to increase users' trust in these systems. Additionally, the European Union included in their regulation about the protection of natural persons with regard to the processing of personal data a new directive about the need of explanations to ensure fair and transparent processing in automated decision-making systems.</p>
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