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Making sense of AI systems development


Dolata, Mateusz; Crowston, Kevin (2024). Making sense of AI systems development. IEEE Transactions on Software Engineering, 50(1):123-140.

Abstract

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

Abstract

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:Artificial Intelligence, Software, Probabilistic Logic, Companies, Training, Cognition, Task Analysis, Artificial Intelligence, Empirical Study, Industry, Social Issues, Software Engineering, Systems Development
Scope:Discipline-based scholarship (basic research)
Language:English
Date:1 January 2024
Deposited On:08 Dec 2023 10:16
Last Modified:27 Jun 2024 03:44
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0098-5589
OA Status:Green
Publisher DOI:https://doi.org/10.1109/tse.2023.3338857
Related URLs:https://doi.ieeecomputersociety.org/10.1109/TSE.2023.3338857 (Publisher)
Other Identification Number:merlin-id:24182
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-No Derivatives 4.0 International (CC BY-ND 4.0)