Applying AI in Practice: Key Challenges and Lessons Learned

Lukas Fischer; Lisa Ehrlinger; Verena Geist; Rudolf Ramler; Florian Sobieczky; Werner Zellinger; Bernhard Moser

doi:10.1007/978-3-030-57321-8_25

Applying AI in Practice: Key Challenges and Lessons Learned

Lukas Fischer
, Lisa Ehrlinger
, Verena Geist
, Rudolf Ramler
, Florian Sobieczky
, Werner Zellinger
, Bernhard Moser

Institut für Anwendungsorientierte Wissensverarbeitung

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.

Originalsprache	Englisch
Titel	Machine Learning and Knowledge Extraction
Herausgeber*innen	Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl
Erscheinungsort	Cham
Verlag	Springer International Publishing
Seiten	451-471
Seitenumfang	21
Band	12279
ISBN (Print)	978-3-030-57321-8
DOIs	https://doi.org/10.1007/978-3-030-57321-8_25
Publikationsstatus	Veröffentlicht - Aug. 2020

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	12279 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Wissenschaftszweige

202007 Computer Integrated Manufacturing (CIM)
102001 Artificial Intelligence
102006 Computer Supported Cooperative Work (CSCW)
102010 Datenbanksysteme
102014 Informationsdesign
102015 Informationssysteme
102016 IT-Sicherheit
102019 Machine Learning
102022 Softwareentwicklung
102025 Verteilte Systeme
102033 Data Mining
102035 Data Science
505002 Datenschutz
509018 Wissensmanagement

JKU-Schwerpunkte

Digital Transformation

Zugriff auf Dokument

10.1007/978-3-030-57321-8_25

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Dieses zitieren

Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobieczky, F., Zellinger, W., & Moser, B. (2020). Applying AI in Practice: Key Challenges and Lessons Learned. in A. Holzinger, A. Holzinger, P. Kieseberg, A. M. Tjoa, E. Weippl, & E. Weippl (Hrsg.), Machine Learning and Knowledge Extraction (Band 12279, S. 451-471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12279 LNCS). Springer International Publishing. https://doi.org/10.1007/978-3-030-57321-8_25

Fischer, Lukas ; Ehrlinger, Lisa ; Geist, Verena et al. / Applying AI in Practice: Key Challenges and Lessons Learned. Machine Learning and Knowledge Extraction. Hrsg. / Andreas Holzinger ; Andreas Holzinger ; Peter Kieseberg ; A Min Tjoa ; Edgar Weippl ; Edgar Weippl. Band 12279 Cham : Springer International Publishing, 2020. S. 451-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Applying AI in Practice: Key Challenges and Lessons Learned",

abstract = "The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.",

author = "Lukas Fischer and Lisa Ehrlinger and Verena Geist and Rudolf Ramler and Florian Sobieczky and Werner Zellinger and Bernhard Moser",

year = "2020",

month = aug,

doi = "10.1007/978-3-030-57321-8\_25",

language = "English",

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volume = "12279",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer International Publishing",

pages = "451--471",

editor = "Andreas Holzinger and Andreas Holzinger and Peter Kieseberg and Tjoa, \{A Min\} and Edgar Weippl and Edgar Weippl",

booktitle = "Machine Learning and Knowledge Extraction",

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Fischer, L, Ehrlinger, L, Geist, V, Ramler, R, Sobieczky, F, Zellinger, W & Moser, B 2020, Applying AI in Practice: Key Challenges and Lessons Learned. in A Holzinger, A Holzinger, P Kieseberg, AM Tjoa, E Weippl & E Weippl (Hrsg.), Machine Learning and Knowledge Extraction. Bd. 12279, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 12279 LNCS, Springer International Publishing, Cham, S. 451-471. https://doi.org/10.1007/978-3-030-57321-8_25

Applying AI in Practice: Key Challenges and Lessons Learned. / Fischer, Lukas; Ehrlinger, Lisa; Geist, Verena et al.
Machine Learning and Knowledge Extraction. Hrsg. / Andreas Holzinger; Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Edgar Weippl; Edgar Weippl. Band 12279 Cham: Springer International Publishing, 2020. S. 451-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12279 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Applying AI in Practice: Key Challenges and Lessons Learned

AU - Fischer, Lukas

AU - Ehrlinger, Lisa

AU - Geist, Verena

AU - Ramler, Rudolf

AU - Sobieczky, Florian

AU - Zellinger, Werner

AU - Moser, Bernhard

PY - 2020/8

Y1 - 2020/8

N2 - The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.

AB - The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.

UR - https://www.scopus.com/pages/publications/85090177992

U2 - 10.1007/978-3-030-57321-8_25

DO - 10.1007/978-3-030-57321-8_25

M3 - Conference proceedings

SN - 978-3-030-57321-8

VL - 12279

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 451

EP - 471

BT - Machine Learning and Knowledge Extraction

A2 - Holzinger, Andreas

A2 - Kieseberg, Peter

A2 - Tjoa, A Min

A2 - Weippl, Edgar

PB - Springer International Publishing

CY - Cham

ER -

Fischer L, Ehrlinger L, Geist V, Ramler R, Sobieczky F, Zellinger W et al. Applying AI in Practice: Key Challenges and Lessons Learned. in Holzinger A, Holzinger A, Kieseberg P, Tjoa AM, Weippl E, Weippl E, Hrsg., Machine Learning and Knowledge Extraction. Band 12279. Cham: Springer International Publishing. 2020. S. 451-471. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-57321-8_25