Uncertainty reducing and handling strategies in ML development projects

Dietz, Johann ; Glaser, Karoline ; Höhle, Hartmut

URL: https://aisel.aisnet.org/icis2021/is_design/is_des...
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: 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
Page range: 1-17
Conference title: ICIS 2021
Location of the conference venue: Austin, TX, Hybrid
Date of the conference: 12.-15.12.2021
Publisher: Valacich, Joe ; Barua, Anitesh ; Wright, Ryan ; Kankanhalli, Atreyi ; Li, Xitong ; Miranda, Shaila
Place of publication: Atlanta, GA
Publishing house: AISeL
ISBN: 978-1-7336325-9-1
Publication language: English
Institution: Business School > Enterprise Systems (Höhle 2017-)
Subject: 004 Computer science, internet
Keywords (English): machine learning , ML development , ML uncertainties , software development uncertainties , uncertainty reducing , uncertainty handling , case study
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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