AI in Business and Society
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Paper Number
1897
Paper Type
Completed
Description
AI incidents, often resulting from the complex interplay of algorithms, human agents, and situations, violate norms and can cause minor or catastrophic errors. This study systematically examines these incidents by developing a typology of AI failure and linking these modes to AI task types. Using a computationally intensive grounded theory approach, we analyzed 466 unique reported real-world AI incidents from 2013 to 2023. Our findings reveal an AI failure typology with six modes, including artifact malfunction, artifact misuse, algorithmic bias, agency oversight, situational unresponsiveness, and value misalignment. Furthermore, we explore the relationship between these failure modes and the tasks performed by AI, uncovering four propositions that provide a framework for future research. Our study contributes to the literature by offering a more holistic perspective on the challenges faced by AI-powered systems, beyond the critical challenges of fairness, transparency, and responsibility noted by the literature.
Recommended Citation
Zhan, Xinhui; Sun, Heshan; and Miranda, Shaila M., "How Does AI Fail Us? A Typological Theorization of AI Failures" (2023). ICIS 2023 Proceedings. 25.
https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/25
How Does AI Fail Us? A Typological Theorization of AI Failures
AI incidents, often resulting from the complex interplay of algorithms, human agents, and situations, violate norms and can cause minor or catastrophic errors. This study systematically examines these incidents by developing a typology of AI failure and linking these modes to AI task types. Using a computationally intensive grounded theory approach, we analyzed 466 unique reported real-world AI incidents from 2013 to 2023. Our findings reveal an AI failure typology with six modes, including artifact malfunction, artifact misuse, algorithmic bias, agency oversight, situational unresponsiveness, and value misalignment. Furthermore, we explore the relationship between these failure modes and the tasks performed by AI, uncovering four propositions that provide a framework for future research. Our study contributes to the literature by offering a more holistic perspective on the challenges faced by AI-powered systems, beyond the critical challenges of fairness, transparency, and responsibility noted by the literature.
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