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Title
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AIMLAI : Advances in Interpretable Machine Learning and Artificial Intelligence
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Author
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Abstract
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| Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable and explainable AI. AIMLAI aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, Infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters. AIMLAI envisions to become a discussion venue for the advent of novel interpretable algorithms and explainability modules that mediate the communication between complex ML/AI systems and users. |
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Language
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English
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Source (book)
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31st ACM International Conference on Information and Knowledge, Management (CIKM), OCT 17-21, 2022, Atlanta, GA
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Publication
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New york
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Assoc computing machinery
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2022
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ISBN
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978-1-4503-9236-5
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DOI
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10.1145/3511808.3557491
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Volume/pages
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(2022)
, p. 5160
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ISI
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001074639605047
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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