2023 Fiscal Year Final Research Report
Explanation-guided Machine Learning Model Development
| Project/Area Number |
20K19860
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Osaka University |
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Principal Investigator |
Hara Satoshi 大阪大学, 産業科学研究所, 准教授 (40780721)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | 機械学習 / 深層学習 / 説明可能AI |
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| Outline of Final Research Achievements |
In this reserach project, we focused on the development of "Explanation-guided model development," which provides appropriate advice to developers on how to construct better machine learning models, leveraging Explainable AI technology as its foundation. Creating highly accurate machine learning models at a level suitable for practical use is not always straightforward, and there can be significant variations in the accuracy of models produced depending on the skill level of the developers.
In this project, we developed methods for data cleansing to improve model performance, its extesion for similarity-based explanation, as well as model correction techniques based on partial model explanation.
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| Free Research Field |
機械学習
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| Academic Significance and Societal Importance of the Research Achievements |
研究成果の学術的意義としては「説明駆動モデル開発という新たな機械学習モデルの開発の仕組みの提案」、そして「XAI技術のさらなる発展による機械学習モデルの解釈性の向上」があげられる。これらにより、モデルを効率的に改善する方法や、モデルがなぜ特定の予測や判断を下したのかを理解することが容易になり、モデルへの信頼性向上が期待される。
研究成果の社会的意義としては「高性能なモデルが効率的に開発可能になることで、機械学習モデルの社会的な活用がより一層進む」ことがあげられる。
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