World’s First Technology to Automatically Restore AI Accuracy: “High Durability Learning” for Realizing Safe and Trustworthy AI

While the number of companies using Artificial Intelligence (AI) for their business is increasing, a new problem has appeared, which is the “deterioration of AI model accuracy during long-time system operation.” In order to solve this issue, Fujitsu Laboratories’ Artificial Intelligence Laboratory developed a groundbreaking technology to detect the deterioration of AI model accuracy and restore it automatically. We interviewed the project members who invented this world’s first technology called “High Durability Learning,” about its development process and excellent features.

Posted on February 13, 2020

Researcher Interview


  • Katsuhito Nakazawa

    Research Manager
    Trusted AI Project
    Artificial Intelligence Laboratory

  • Yasuto Yokota

    Trusted AI Project
    Artificial Intelligence Laboratory

  • Yuhei Umeda

    Research Manager
    Autonomous Machine Learning Project
    Artificial Intelligence Laboratory

Issue in AI system operation that AI model accuracy deteriorates

Seeing the recent global trend toward the full-scale use of AI systems, many companies are rushing to raise work efficiency and productivity by introducing AI systems. However, we see a new problem occurring, which is the gradual deterioration of AI model accuracy during a target system operation. Why does the accuracy deteriorate? Katsuhito Nakazawa, Research Manager of Trusted AI Project in Artificial Intelligence Laboratory says as follows:

“When we develop an AI system, we firstly create an AI model which is used for a target business system by preparing the training data in that field and repeating machine learning. Then, we input the target business data into this AI model to make it output the processing results such as predictions or decisions on given cases. However, over the course of long-time operation, its external environment changes and the content of new data input into the AI model keeps changing from its initial training data. As a result, the AI model becomes outdated and its accuracy deteriorates.”

This kind of problems may arise in various fields where AI systems are expected to be introduced and used. For example, financial institutions are examining AI adoption for corporate credit risk assessment. As AI evaluates the loan risk based on financial statements, etc., due to fluctuations of exchange rates and interest rates, AI model’s prediction accuracy lowers.

In order to solve these issues, we have to maintain the AI model regularly. However, it is not so easy.

“For keeping AI accuracy, we need to perform retraining by using training data with annotations. However, it needs a lot of costs and time to annotate manually for retraining. On the other hand, if we keep using it without doing maintenance for a long time, AI accuracy goes down and it becomes impossible for AI to make a correct prediction, which may cause serious damage.” (Nakazawa)

New technology to automate detection of deterioration and restoration of AI accuracy

These issues of AI system operation have already been discussed in several industries and we hear them insisting the needs of concrete prevention measures. In this situation, Fujitsu Laboratories began to develop a technology to automatically recover the AI accuracy when it deteriorates. As a result, we announced the world’s first technology named “High Durability Learning” in October, 2019.

With a conventional AI operation method, unless correct labels are put to the input data in system operation, it becomes difficult to judge whether the AI accuracy is lowering or not. Therefore, experts had to add correct labels to newly input data regularly and confirm the accuracy. If they found its deterioration, they retrained the model. This work required huge costs. On the contrary, “High Durability Learning” uses a mathematical space called a “DT space (Durable Topology Space)” where the data distribution characteristics are shown as masses of data, and while monitoring the changes of the shapes of these masses with time, it determines the correct answer (annotation) to each piece of input data. Based on this annotation results, annotation is performed again in the original data space. Then, comparing the difference from the initial classification, it can automatically estimate the deterioration rate of an AI model.

Moving the data from a “data space where it is difficult to estimate the accuracy deterioration” to a “data space where it is possible to estimate the accuracy deterioration”

Like this, “High Durability Learning” enables to detect the accuracy deterioration and recover the accuracy automatically without performing annotation by experts and retraining. According to our verification results using an actual AI model, the rate of detection error was only 3%. And when we used a conventional method, the prediction accuracy of the AI model lowered to 69% from its initial rate of 91% after one year. On the contrary, when we apply “High Durability Learning” technology, the AI model’s prediction accuracy can be kept at 89%. As automatic restoration function can decrease the retraining frequency by 90% and retraining costs by 80% or more, it becomes possible to cut the total retraining costs to one hundredth or less.” (Nakazawa)

Another and the most significant feature of “High Durability Learning” that we emphasized in its development is “versatility.” As High Durability Learning system is not environment-dependent, it can be used for various AI systems as an add-on function.

“As various AI models are used in business, we considered that it is important to develop a function which can be applied to any kind of AI model without limiting the type of input data and can be used directly as an add-on with versatility. We have already finished verification by using representative AI models and algorithms such as a deep neural network (DNN), support vector machine (SVM) and Random Forest, and are planning to expand its application range further.” (Nakazawa)

Project started with Ideathon (brainstorming) for issue-solving technologies

Now, “High Durability Learning” is drawing attention as a technology to solve issues in AI system operation. This R&D project was launched by an internal working group which was set up in the autumn of 2018. Based on the research results of this working group, the research group was formed and has been led by Research Manager Nakazawa, who had been engaged in AI quality management and Explainable AI-related technologies such as Knowledge Graph.

“Fujitsu is not only an IT company but also a manufacturing company for which high levels of technological strictness is required. Therefore, I considered that it is our role to present a firm solution for AI accuracy deterioration problem. We started with a brainstorming session participated by researchers of Trusted AI Project for sharing ideas, and came up with about 20 seeds of ideas. After discussing their feasibility for about two months, we started the R&D project in full swing.” (Nakazawa)

Through these activities, we came up with the said “DT space” technology. And, it was Yuhei Umeda, Research Manager of Autonomous Machine Learning Project, who cast this idea into shape. The feature of this technology is to grasp the data changes more mathematically and topologically.

“In a data space of general machine learning, we can only trace the movement of each piece of data one by one. However, in order to grasp the characteristics of the whole data, we need to grasp a set of data as a mass, and to trace time-series changes in its shape mathematically with a bird’s-eye view. We payed attention to the information on the shapes of two masses of classified data and tried to analyze them by using “Topological data analysis (TDA),” and continued the development of a machine learning technology. Then we reached the concept of a “DT space.” (Umeda)

It was Yasuto Yokota, one of staring members of this project, who took charge of designing and implementation of an algorithm for identifying the data characteristics with a high degree of accuracy, by using this DT space. He has experience in development of applied technology of Deep Learning and made efforts to resolve relevant issues in cooperation with Fujitsu’s business unit. Making the most of his implementation ability, he created its program.

“We tried to design the algorithm for extracting important parts from the topological mass of data. It was really tough for us to create a prototype without design document and to brush up through trial and error.” (Yokota)

World’s first technology developed by researchers with diverse talents

We could finally announce “High Durability Learning” as the world’s first technology. We really feel grateful for our company’s environment that allows us to promote R&D on basic technologies in collaboration with Fujitsu’s business units who are excellent in grasping customer needs.

Yokota continues as follows.
“Many companies and academic institutions are working on research and development of AI. And, most of them focus on the development of machine learning algorithms and AI models. In this situation, as Fujitsu Laboratories is a member of the Fujitsu Group which have many business divisions, we can get the information on problems that our customers are facing in their AI system operation, and take an approach from the aspect of “Operation.”

Fujitsu Laboratories has so many researchers who have abundant knowledge about the situations of on-site work, as well as mathematics, algorithms and designing for system implementation. They also have diverse professional backgrounds. Not only researchers with rich experience in leading-edge technologies but also ex-engineers recruited through Fujitsu’s business units, or those employed as mid-career recruits like Yokota who worked outside the Fujitsu Group before. The “High Durability Learning” technology was the fruit of collective efforts made by these wide variety of people.

At the project, we are now advancing verification for applying “High Durability Learning” to AI systems in various fields such as for credit risk assessment in financial industry, merchandise image classification in retail industry, and character recognition of delivery slips in distribution and logistics industry. We are also planning to enhance other functions such as a dashboard to visualize the result of AI accuracy monitoring.

“If we can avoid AI accuracy deterioration and assure stable system operation, companies will more actively introduce and use AI systems. We are convinced that our mission is to realize trustworthy AI systems that can adapt to the changes in socio-economic situations and make a correct decision with fairness and explainability.” (Nakazawa)


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