FUJITSU Project:

Title Enhancing explainability of modern AI
Industrial Partner FUJITSU LIMITED
Fujitsu, a leading Japanese information and communication technology (ICT)company, offers a full range of technology-related products, solutions, and services.
We have produced numerous and diverse innovative results through IT and have beenleading the world by providing new value to individuals, society, and businesses.
Fujitsu, number 1 in Japan, is the world's tenth largest IT service provider.
Industrial Mentor
Hiroyuki Higuchi, Ph.D., Fujitsu Limited.
Background
Artificial intelligence (AI) has been increasingly introduced into various industries.
Nevertheless, in many cases, effective utilization is not achieved, mainly because AI
provides users no information related to its judgments. Therefore, a strong need exists
for modern and practical AI to offer explainability to the users and present themreasons to justify the AI rationale [1].
Fujitsu has developed “Wide Learning”: explainable AI (XAI) that combines
discovery science and machine learning [2,3]. Figure 1 depicts the principle of Wide
Learning with an example of marketing data. An operator enters multiple aspects ofdata of a customer who has purchased a product. Wide Learning exhaustively extracts
important combinations of data items as hypotheses hidden in various data anddetermines the likelihood of new customers purchasing a product. The important
hypotheses are explained as reasons supporting the decision.
Table 1 presents a comparison between Wide Learning and Deep Learning [4].

Fig. 1 Principle of Wide Learning.
Table 1 Wide Learning and Deep Learning
Wide Learning

Deep Learning

Process of scientific discovery

Simulation of a neural network

Tabular dataImages and soundFrom several dozen to several

At least one thousand to several

hundred records

tens of thousands of records

Explainability

XAI (explainable AI)

Black box

OutputClassifications, forecasting, andClassifications and forecasting

Operatingprinciple
Suitable data
type
Amount of data

action plans
Hardware

Computer with a generic CPU

requirements(Even a notebook can be used)

Parallel computers such as GPUs

Wide Learning has been applied to industrial fields such as manufacturing, finance,and healthcare. However, a gap separating explainable AI and humans apparently
remains: AI’s explanations and humans’ understanding. Sometimes the explanations
which Wide Learning offers are difficult for humans to understand. Therefore, someexperts must reinterpret them or summarize them for non-experts to understand themeasily, which requires great expenses in terms of costs and time in field operations.
Project overview
In the project you will develop an enhanced view of explanations of Wide Learningassisted by mathematics. For purposes of enhancement, you should consider several
aspects such as those below.
1. Convincingness: Explanations must be convincing and accepted by users.

Therefore, evaluating or estimating the explanations’ likelihood of convincing
users is important. That evaluation will require mathematical modeling of theexplanation’s characteristic of “convincingness”. The simplest modeling would be
to use the length of an explanation based on the assumption that a short
explanation is easy to understand. However, better modeling might be devised byassuming more practical recognition models.
2. Variety: An explanation for a judgment is not always unique. Often, more than one
perspective is necessary to explain a judgment. The phenomenon that manyequally good models explain some given data well is called the Rashomon effect[5]. A set of many equally good models is called a Rashomon set [6]. Although aRashomon set gives variety in explanation, a tradeoff between variety andsimplicity (convincingness) should be considered.
3. Discoverability: When an explanation is not convincing to a user, it is not always
because it is a bad explanation. It might be a new finding that a user has nevernoticed. In that sense, an explanation must also be evaluated in terms ofdiscoverability.
By exploring one or more aspects of enhancement, you will develop a new way ofgiving explanations of Wide Learning.
Expectations
When participating in this project, to enhance the explainability of Wide Learning, youwill be expected to devise a new user interface of Wide Learning and then implement
it. The implementation should be scalable to a large number of data items, for example,
more than 100 data items. We look forward to welcoming students who are interested
in the Wide Learning Website “Hello, Wide Learning!” [2]. In addition, students whoare interested in answers to the following questions are welcomed.
✓ What is explainable AI?
✓ What is the gap separating AI and humans?
✓ What are good interactions between AI and humans?
✓ How can AI help humans discover new findings?
Requirements
Programming skills in python and, preferably, C or C++.
References

[1] S. R. Islam et al., “Explainable Artificial Intelligence Approaches: A Survey,”
arXiv:2101.09429, 2021.
https://arxiv.org/abs/2101.09429[2] Wide Learning Website, "Hello, Wide Learning!".
https://widelearning.labs.fujitsu.com/en/[3] H. Iwashita et al., “Efficient Constrained Pattern Mining Using Dynamic Item
Ordering for Explainable Classification,” arXiv:2004.08015, 2020.
https://arxiv.org/abs/2004.08015[4] A. Krizhevsky et al., “ImageNet Classification with Deep Convolutional NeuralNetworks,” In Advances in Neural Information Processing Systems, 25, 2012.
[5] L. Breiman, “Statistical modeling: The two cultures (with comments and a rejoinderby the author),” Statistical Science, 16(3): 199-231, 2001.
[6] R. Xin et al., “Exploring the Whole Rashomon Set of Sparse Decision Trees,” In
Proceedings of the 36th Intl. Conf. on Neural Information Processing Systems, 2022.