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

[2L5-OS-19a-02] AITHub: FAIR-oriented Sharing Platform for AI Quality Assessment Methods

〇Takao Nakagawa¹, Kenichirou Narita¹, Yuta Iwase¹, Kyoungsook Kim¹ (1. National Institute of Advanced Industrial Science and Technology)

Keywords:AI System, Quality Assurance

For industrial application of machine learning technology, it is important to assess the quality of ML components. Therefore, various associations provided standards and guidelines for this theme. Since these documents are written for human, it is mandatory for practitioners to manually select and implement appropriate evaluation methods. While some of these methods are provided in executable form, factors such as different tasks, model architectures, data formats, or framework versions make practical application difficult. In this study, we analyze challenges that prevent the findability and reusability of AI quality assessment methods and propose a FAIR-oriented platform for sharing them.