[Trusted quality AI systems] Year Started : 2021

Hisashi Kashima

Human Computation for Human-AI Collaboration

Research Director
Hisashi Kashima

Professor
School of Informatics
Kyoto University

Collaborator
Hiromi Arai Unit Leader
Center for Advanced Intelligence Project
RIKEN
Satoshi Oyama Associate Professor
Faculty of Information Science and Technology
Hokkaido University
Junichiro Mori Associate Professor
Graduate School of Information Science and Technology
The University of Tokyo
Outline

We aim to establish the foundation of human computation for designing trustworthy human-AI collaborative systems by (i) human-in-the-loop machine learning for human-AI collaborative data analytics, (ii) defining and optimizing reliability and trust for human computation systems, (iii) addressing ethical issues for social acceptance of human computation, and (iv) supporting human intellectual and creative activities and developing human capabilities through human computation.

Shinya Takamaeda

D3-AI: Distributed AI for Dynamic and Diverse Environments

Research Director
Shinya Takamaeda

Associate Professor
Graduate School of Information Science and Technology
The University of Tokyo

Collaborator
Masaaki Imaizumi Associate Professor
Graduate School of Arts and Sciences
The University of Tokyo
Tomoya Kitani Associate Professor
Academic Institute
Shizuoka University
Hideki Takase Associate Professor
Graduate School of Information Science and Technology
The University of Tokyo
Kentaro Yoshioka Assistant Professor
Faculty of Science and Technology
Keio University
Outline

We develop fundamental technologies for energy-efficient distributed AI systems based on federated learning for respecting the diversity of users and data and the spatial and temporal environmental variations. We define D3-AI as the distributed AI with four reliability capabilities: privacy, fairness, dynamic adaptability, and energy efficiency. We pursue researches on the fundamental technologies and applications of D3-AI in collaboration with machine learning theory, computer architecture, IoT platform, and data processing.

Ichiro Takeuchi

Assessing Reliability of AI-driven Hypotheses in Static and Dynamic Environments and Its Application to Medical Science

Research Director
Ichiro Takeuchi

Professor
School of Engineering
Nagoya University

Collaborator
Koichi Ohshima Professor
Department of Medicine
Kurume University
Atsushi Kawaguchi Professor
Department of Medicine
Saga University
Jun Sakuma Professor
Faculty of Engineering, Information and Systems
University of Tsukuba
Shigeyuki Matsui Professor
Department of Medicine
Nagoya University
Outline

We develop a mathematical and computational framework for assessing the reliability of AI-driven hypotheses for AI-driven science and technology by extending the traditional statistical hypothesis testing framework. Our main focus is to develop new theories, algorithms, and software for statistical hypothesis testing that is valid for adaptively constructed AI-driven hypotheses. We study AI-driven hypotheses in static and dynamic environments and pursue how to evaluate their reliability from both theoretical and practical perspectives. We develop a protocol for evaluating the reliability of AI medical technology based on the mathematical and computational framework and demonstrate the protocol through the development of an AI-based pathological diagnosis system for malignant lymphoma.

Seiji Yamada

Trust Interaction Design for Convincing Human-AI Cooperative Decision Making and its Social Penetration

Research Director
Seiji Yamada

Professor
National Institute of Informatics
Research Organization of Information and Systems

Collaborator
Tetsuo Ono Professor
Faculty of Information Science and Technology
Hokkaido University
Hirokazu Kumazaki Professor
Institute of Biomedical Sciences
Nagasaki University
Kazunori Terada Associate Professor
Faculty of Engineering
Gifu University
Takashi Hara Professor
Faculty of Engineering
Gifu University
Outline

Our purpose is to build human-AI cooperative decision-making systems for trustworthy AI. In this project, we develop trust calibration AI (TCAI) which can detect over/under-trust by monitoring humans’ selection behaviors in human-AI collaboration and adaptively facilitate humans’ trust calibration by themselves. The TCAI uses calibration cues as stimuli that facilitate humans’ voluntary trust calibration. Finally, we apply the TCAI to human-AI cooperative imaging diagnosis and medical examination.

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Program

  • CREST
  • PRESTO
  • ACT-I
  • ERATO
  • ACT-X
  • ACCEL
  • ALCA
  • RISTEX
  • manual
  • AIP Network Lab
  • Global Activities
  • Diversity
  • SDGs
  • OSpolicy
  • Yuugu
  • Questions