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Uncertainty reducing and handling strategies in ML development projects
Dietz, Johann
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Glaser, Karoline
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Höhle, Hartmut
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URL:
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https://aisel.aisnet.org/icis2021/is_design/is_des...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2021
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Book title:
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Proceedings of the 42nd International Conference on Information Systems, ICIS 2020, Building Sustainability and Resilience with IS: A Call for Action, Austin, Texas, December 12-15, 2021
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Page range:
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1-17
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Conference title:
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ICIS 2021
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Location of the conference venue:
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Austin, TX, Hybrid
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Date of the conference:
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12.-15.12.2021
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Publisher:
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Valacich, Joe
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Barua, Anitesh
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Wright, Ryan
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Kankanhalli, Atreyi
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Li, Xitong
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Miranda, Shaila
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Place of publication:
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Atlanta, GA
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Publishing house:
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AISeL
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ISBN:
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978-1-7336325-9-1
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Publication language:
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English
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Institution:
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Business School > Enterprise Systems (Höhle 2017-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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machine learning , ML development , ML uncertainties , software development uncertainties , uncertainty reducing , uncertainty handling , case study
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Abstract:
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Although prior literature suggested that machine learning (ML) development can suffer strongly from uncertainty, it neglected to unveil the specific uncertainties arising in ML development projects and to understand their impact on the development process. To address this gap, we conduct an exploratory case study based on 62 interviews with ML experts from a multinational software provider. Our study reveals that uncertainty management strategies either target an uncertainty’s reducible or irreducible part and can thus be divided into reducing and handling strategies. We develop a model that shows at which stage of the ML development process each uncertainty is addressed and how as well as by whom the respective reducing and handling strategies are executed. The study mainly contributes to literature on ML development and on uncertainties in software development by unveiling the impact of uncertainty reducing and handling strategies triggered by ML specific uncertainties on the ML development project.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Dietz, Johann
;
Glaser, Karoline
;
Höhle, Hartmut
Google Scholar:
Dietz, Johann
;
Glaser, Karoline
;
Höhle, Hartmut
ORCID:
Dietz, Johann, Glaser, Karoline and Höhle, Hartmut  ORCID: https://orcid.org/0000-0001-8117-0105
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