Abstract
There is always a risk of misjudgment in machine-learning based AI system that might cause monetary or safety problems to users. For this reason, it is an essential human-centered AI issue to let AI system users recognize the risk rightly and provide an opportunity to evaluate of AI system quality including the risk mitigation capability. If a user finds that an AI system is beneficial enough and the misjudgment risk is manageable, he can build trust in the system and use it. However, such user commitment has not been focused in discussion of AI trust or AI quality evaluation. This paper proposes a framework of AI system evaluation by users. The framework allows them to evaluate AI system’s quality-in-use components including risk related attributes in comprehensive manner. Based on the framework we conducted a questionnaire survey about the evaluation of personal AI systems and confirmed that users’ feedback could prioritize the influence of the quality-in-use components to their intention to use, and that could help the system provider how to implement the risk mitigation capability.
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Ogawa, R., Shima, S., Takemura, T., Fukuzumi, Si. (2023). A Study on Trust Building in AI Systems Through User Commitment. In: Mori, H., Asahi, Y. (eds) Human Interface and the Management of Information. HCII 2023. Lecture Notes in Computer Science, vol 14015. Springer, Cham. https://doi.org/10.1007/978-3-031-35132-7_42
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DOI: https://doi.org/10.1007/978-3-031-35132-7_42
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