知能ロボティクス領域のRACHARAK助教の研究課題がGoogleの「Faculty awards to support machine learning courses, diversity, and inclusion at universities」に採択

 知能ロボティクス領域のRACHARAK, Teeradaj助教の研究課題がGoogleの「Faculty awards to support machine learning courses, diversity, and inclusion at universities」に採択されました。

 このアワードプログラムは、大学等の教育機関に所属する教員を対象に、大学生・大学院生向けの機械学習の教育コンテンツ開発、もしくはコンピューターサイエンスの領域において少数派にあたる人々の参加を促すダイバーシティへの取り組みを支援します。Google は、このアワードプログラムを通じ、世界各地の教育機関と協力しながら、AI/機械学習教育の支援を拡大することを目指しています。

知能ロボティクス領域 RACHARAK, Teeradaj助教


Responsible AI with ML/DL Libraries

Machine learning (ML)-based AI systems, especially Deep Learning (DL), have demonstrated remarkable learning capabilities. Unfortunately, current AI systems are inherently hard to gain trust from their users. With responding to this issue, a lot of ML/DL researchers have been focusing on improving the Fairness, Accountability, and Transparency (FAccT) of ML/DL models in addition to their performance. More recently, FAccT is a fast-growing and important area of the ML/DL research. Nonetheless, there is still no standard agreement on how FAccT technologies should be designed and developed. This project aims to study this open problem by jointly collaborating with the Google TensorFlow team. For this purpose, state-of-the-art approaches of FAccT AI from a ML/DL perspective are revisited. After that, the design and development of Responsible AI technologies are studied to provide trust and confidence in AI - to be sure that they are fair, accountable, and transparent.