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
Algorithmic accountability obligates developers to justify themselves for their artificial intelligence (AI)-based systems. Despite this positive effect, there is still insufficient information system (IS) research on how developers’ perceived algorithmic accountability can be increased and how it affects AI development projects. Within our qualitative interview study, we asked 25 developers about algorithmic accountability during their AI development projects. We observe that developers’ perceived algorithmic accountability depends on organizational factors (i.e., quality management, working method, company structure, and the facets of AI) and personal factors (i.e., understanding of AI-based systems and algorithmic accountability), leading to more scrutinized AI-based systems. Overall, this study contributes to IS development (ISD) research by providing transparency on how developers’ perceived algorithmic accountability is affected and how it affects AI development projects. These findings are also relevant for practitioners, as we suggest how they can shape their work environment to promote the positive effects of algorithmic accountability.
Recommended Citation
Bartsch, Sebastian; Milani, Verena; Adam, Martin; and Benlian, Alexander, "Algorithmic Accountability: What Does it Mean for AI Developers and How Does it Affect AI Development Projects" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 6.
https://aisel.aisnet.org/hicss-57/os/ai_and_organizing/6
Algorithmic Accountability: What Does it Mean for AI Developers and How Does it Affect AI Development Projects
Hilton Hawaiian Village, Honolulu, Hawaii
Algorithmic accountability obligates developers to justify themselves for their artificial intelligence (AI)-based systems. Despite this positive effect, there is still insufficient information system (IS) research on how developers’ perceived algorithmic accountability can be increased and how it affects AI development projects. Within our qualitative interview study, we asked 25 developers about algorithmic accountability during their AI development projects. We observe that developers’ perceived algorithmic accountability depends on organizational factors (i.e., quality management, working method, company structure, and the facets of AI) and personal factors (i.e., understanding of AI-based systems and algorithmic accountability), leading to more scrutinized AI-based systems. Overall, this study contributes to IS development (ISD) research by providing transparency on how developers’ perceived algorithmic accountability is affected and how it affects AI development projects. These findings are also relevant for practitioners, as we suggest how they can shape their work environment to promote the positive effects of algorithmic accountability.
https://aisel.aisnet.org/hicss-57/os/ai_and_organizing/6