Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This study explores how practitioners identify and manage AI project-related risks to reduce AI project failures. Specifically, through a qualitative research study involving 16 data science practitioners, this study presents insights into how practitioners articulate and mitigate the risk of AI project failure. A thematic analysis of this study identified six key themes (Ethical risk, BlackBox Models, Data Privacy, Data Storage, Financial Risks, and Success criteria). Further analysis explored drivers for identifying and mitigating these risks. Specifically, it was found that agency (consumer and institutional-driven) and social/cultural capital (such as management hierarchy and domain knowledge) legitimized specific AI project risks and were key drivers in ensuring risks were identified and mitigated. Results from this research suggest that future research should explore different social and cultural perspectives since these perspectives can impact the articulation of risk and how these risks can be ultimately managed within an AI project context.
Recommended Citation
Lahiri, Sucheta and Saltz, Jeffrey, "AI Project and Deployment Risk: Articulation and Legitimization" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 7.
https://aisel.aisnet.org/hicss-57/os/ai_and_organizing/7
AI Project and Deployment Risk: Articulation and Legitimization
Hilton Hawaiian Village, Honolulu, Hawaii
This study explores how practitioners identify and manage AI project-related risks to reduce AI project failures. Specifically, through a qualitative research study involving 16 data science practitioners, this study presents insights into how practitioners articulate and mitigate the risk of AI project failure. A thematic analysis of this study identified six key themes (Ethical risk, BlackBox Models, Data Privacy, Data Storage, Financial Risks, and Success criteria). Further analysis explored drivers for identifying and mitigating these risks. Specifically, it was found that agency (consumer and institutional-driven) and social/cultural capital (such as management hierarchy and domain knowledge) legitimized specific AI project risks and were key drivers in ensuring risks were identified and mitigated. Results from this research suggest that future research should explore different social and cultural perspectives since these perspectives can impact the articulation of risk and how these risks can be ultimately managed within an AI project context.
https://aisel.aisnet.org/hicss-57/os/ai_and_organizing/7