[Trusted quality AI systems] Year Started : 2022

Atsushi Shimada

A Development of Reliable Learning Analytics Platform and its Advanced Applications for Teaching and Learning Support

Research Director
Atsushi Shimada

Professor
Faculty of Information Science and Electrical Engineering
Kyushu University

Collaborator
Daisuke Deguchi Associate Professor
Graduate School of Informatics
Nagoya University
Takayoshi Yamashita Professor
College of engineering
Chubu University
Outline

Learning analytics (LA) is research that provides effective support for teaching and learning based on analysis of educational data. This project will develop a new learning analytics platform, named “ReLAX”: Reliable Learning Analytics for X, that realizes immediacy to quickly provide analytics results, persuasiveness by making the analysis processes transparent, and generalizable analytics methodologies for various educational data.

Shohei Shimizu

Causal discovery and its applications for reliable AI

Research Director
Shohei Shimizu

Professor
Faculty of Data Science
Shiga University

Collaborator
Kei Inoue Researcher
Clinical Development Department
SUSMED, Inc
Takehiko Hayashi Chief Senior Researcher
Social Systems Division
National Institute for Environmental Studies
Shingo Fukuma Associate Professor
Graduate School of Medicine
Kyoto University
Outline

Statistical causal inference using causal graphs is essential in improving the explainability, fairness, and performance needed for reliable AI. To perform statistical causal inference, a causal graph needs to be drawn by the analyst, but it often is the case that insufficient domain knowledge is available for this purpose. Then, a causal structure search methodology that uses data to infer a causal graph, i.e., causal discovery, is useful. Thus, we develop methods for causal discovery to infer causal graphs from data and use them to analyze explainability and fairness in the four areas of policy science, environmental studies, preventive medicine, and clinical medicine.

Mahito Sugiyama

Machine Learning That Connects to Symbolic Reasoning

Research Director
Mahito Sugiyama


Associate Professor National Institute of Informatics Principles of Informatics Research Division

Collaborator
Katsumi Inoue Professor
National Institute of Informatics
Research Organization of Information and Systems
Ryosuke Kojima Lecturer
Graduate School of Medicine
Kyoto University
Masaaki Nishino Distinguished Researcher
NTT Communication Science Laboratories
NTT Corporation
Outline

To develop fundamental methodologies of trustworthy AI, we aim at unifying modern machine learning approaches with potentially massive parameters and symbolic reasoning that enjoys high interpretability through the geometric lens. We design and construct machine learning systems inherently connected to symbolic reasoning, which simultaneously solve the problems of the reliability of machine learning and the robustness of symbolic reasoning.

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Program

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