[2L5-OS-19a] OS-19

[2L5-OS-19a-01] Safety quality management methods in the development of AI modules

〇Takaaki Namba¹, Tamao Okamoto¹, Yoshihiro Nakabo², Yasushi Sumi², Bong-Keun Kim², Kiyoshi Fujiwara², Takuya Ogure² (1. AIST / Panasonic Holdings Corp., 2. AIST)

Keywords:Quality, Assessment, Machine Learning, Guideline, Safety

Quality management in the development of AI modules is important due to social demands (laws / regulations / standards / guidelines). However, the methodology has not yet been established. Therefore, we have developed AI quality assessment sheets to clarify how to assess AI safety (harm to humans, economic loss), usefulness, fairness, privacy, and AI security, and to support management. In this paper, we present AI quality assessment sheets based on the "Machine Learning Quality Management Guidelines" as a specific quality assessment method focusing on the development process and AI-specific quality characteristics. Our method enables sharing achieved quality among stakeholders and giving concrete explanations to society as evidence. It is also useful for clarifying ordering conditions, identifying problems, improving the quality,and presenting high-quality added value. We expect that this effort will assist the concretization of quality management methods and accelerate problem solving for "Responsible AI".