Algorithmic Accountability: What Does it Mean for AI Developers and How Does it Affect AI Development Projects

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dc.contributor.authorBartsch, Sebastian
dc.contributor.authorMilani, Verena
dc.contributor.authorAdam, Martin
dc.contributor.authorBenlian, Alexander
dc.date.accessioned2023-12-26T18:48:01Z
dc.date.available2023-12-26T18:48:01Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.702
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other7211e091-04ad-461b-a363-f31149de1f81
dc.identifier.urihttps://hdl.handle.net/10125/107087
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI, Organizing, and Management
dc.subjectartificial intelligence
dc.subjectdevelopers’ perceived algorithmic accountability
dc.subjectorganizational factors
dc.subjectpersonal factors
dc.subjectqualitative interview study
dc.titleAlgorithmic Accountability: What Does it Mean for AI Developers and How Does it Affect AI Development Projects
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractAlgorithmic 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.
dcterms.extent10 pages
prism.startingpage5826

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