Abstract
We propose a system that focuses on consensus building and agreement implementation as the basis for establishing AI trustworthiness. Our approach is new, and we have called it consensus building with an assurance case: it is based on applying open systems dependability engineering to the objective of achieving stakeholder accountability. The experimental validation of the proposed system was conducted on the issue of feature engineering within the context of a project on data-intensive medicine. Our findings show that the online nature of the proposed system is effective in facilitating stakeholders participation and contribution, and logging the consensus building process in a useful manner. Using this approach, stakeholders can achieve a logical understanding of the substance of the consensus and the process by which it was reached, thereby providing assurance that AI safety-related requirements are being met.
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Acknowledgments
The authors would like to thank Naoko Takanashi for her technical assistance implementing the CBAC-based support system, along with Akiko Hanai and Manami Kato, members of RIKEN, as stakeholders in our experimentation. This paper is partially supported by Innovation Platform for Society 5.0 from Japan’s Ministry of Education, Culture, Sports, Science and Technology.
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Yanagisawa, Y., Yokote, Y. (2021). A New Approach to Better Consensus Building and Agreement Implementation for Trustworthy AI Systems. In: Habli, I., Sujan, M., Gerasimou, S., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops. SAFECOMP 2021. Lecture Notes in Computer Science(), vol 12853. Springer, Cham. https://doi.org/10.1007/978-3-030-83906-2_26
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DOI: https://doi.org/10.1007/978-3-030-83906-2_26
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