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020 _a9783030764098
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024 7 _a10.1007/978-3-030-76409-8
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082 0 4 _a621.382
_223
245 1 0 _aExplainable AI Within the Digital Transformation and Cyber Physical Systems
_h[electronic resource] :
_bXAI Methods and Applications /
_cedited by Moamar Sayed-Mouchaweh.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aX, 198 p. 69 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Part 1 Methods used to generate explainable models -- Explainable Artificial Intelligence (XAI) -- intrinsic explainable models -- model-agnostic methods -- Part 2 Evaluation layout and meaningful criteria -- expressive power -- portability evaluation layout -- accuracy evaluation layout -- algorithmic complexity -- fidelity evaluation -- stability evaluation -- representativeness evaluation layout -- local/global explanation -- Part 3 XAI applications within the context of digital transformation and cyber-physical systems -- applications of XAI in decision support tools -- smart energy management -- finance -- telemedicine and healthcare -- critical systems -- e-government -- Conclusion.
520 _aThis book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) in different application domains. Secondly, that it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefitsand requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions. Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems; Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems; Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.
650 0 _aTelecommunication.
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 0 _aQuantitative research.
650 1 4 _aCommunications Engineering, Networks.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Analysis and Big Data.
700 1 _aSayed-Mouchaweh, Moamar.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030764081
776 0 8 _iPrinted edition:
_z9783030764104
776 0 8 _iPrinted edition:
_z9783030764111
856 4 0 _uhttps://doi.org/10.1007/978-3-030-76409-8
912 _aZDB-2-SCS
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