g-RIPS 2023 Fujitsu Group

Design and Development of
Explainable AI with Wide Learning
Group Members:
Lily Ge (Northwestern University)
Yuki Kimura (Musashino University)
Takeshi Nakashima (Musashino University)
Molly Noel (Rensselaer Polytechnic Institute)

Academic Mentor:
Jesica Bauer (Rensselaer Polytechnic Institute)
Industry Mentor:
Dr. Hiroyuki Higuchi (Fujitsu Limited)●Motivation
○
○

●
●

Wide Learning
Our Approach○○

Outline○○●What is Explainable AI?
Explainability Approaches

User selection LASSO
Model selection■ Rademacher complexity● result
Interface Specification andDevelopment■ result
Pilot Survey for New Interface■ resultConclusion2What is Explainable AI?

3

Modern Model● Many modern AI predictions are based on black-box
models/systems and lack explanatory power and conviction.
● This lack has a significant impact on industries such as financeand medicine, which are driven by trust.

black-box

I know theresult, but not
the reason…
prediction

Islam, Sheikh Rabiul, William Eberle, Sheikh Khaled Ghafoor, and Mohiuddin Ahmed. "Explainable artificial intelligence approaches: A survey." arXiv preprint arXiv:2101.09429 (2021).

4

Explanation Approaches“I recommend you do
this, and here is why…”“Based on your situation,
here are some options…”

Recommendation-driven

Hypothesis-driven

AB

Miller, Tim. "Explainable ai is dead, long live explainable ai! hypothesis-driven decision support." arXiv preprint arXiv:2302.12389 (2023).

5

“Hello, Wide Learning!” by Fujitsu Limited● Wide Learning is a form of
the hypothesis-driven
approach● We aim to enhance it anddevelop a more
hypothesis-driven
Explainable AI (XAI) tool

Combination of important items
Oviparous_No
Sized about the same as a cat?_Yes
Breathes using lungs_Yes ∧ Fins_Has
Wings_Does not have ∧ Eats meat_No ∧ Spine_Has
Teeth_Has ∧ Breathes using lungs_Yes
Flies_Does not ∧ Aquatic_No6

How Can We Improve
Wide Learning?

7

Pilot Survey● Open-ended questions to elicit people’s thoughts on theexisting Wide Learning tool
● Anonymized and expected to take approximately 15 minutes tocomplete● Distributed through the #students-grips2023 Slack channel
and received 10 responses● Survey responses informed how we built the new user interface

8Existing Interface of “Hello, Wide Learning!”
Step 1

Step 2

Step 3

Step 4

Step 5Training
1. Upload training data
2. Get important feature combinations generated by Wide Learning
3. Assign weights to the combinations

Testing
4. Upload testing data
5. Make predictions for testing data
9

Our focus
Step 1

Step 2

Step 3

Step 4

Step 5● Wide Learning efficiently identifies important featurecombinations at Step 2● Weights are assigned to these important combinations in Step
3 and are used to make predictions in Step 5● We focus on making improvements to Steps 3-5, by addressing
areas of confusion from the survey responses and incorporating
hypothesis-driven learning
10

Improvement 1:
Feature Selection in LASSO

11

Logistic Lasso● Logistic regression with L1 regularizer to assign weights tocombinations from Step 2● Lasso performs feature selection → some combinations areassigned weight 0● Optimal weights are selected by solving the following problem:

Robert Tibshirani.Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.

12

User Input● In Wide Learning and the other models considered, importantfeature combinations are selected by the model● We include user selection of important combinations in ourLasso model to increase trustworthiness based on this paper:
Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint
arXiv:1611.05940, 2016.

13

User Input● Lasso selects important feature combinations by assigning them anonzero weight● Feature combinations not included in the model are assigned
weight zero
● User can select a feature combination with a zero weight toinclude in the model● The optimal nonzero weight for this combination is found● A feature combination included in the original model is assigned azero weight to keep the same number of nonzero weights
14
Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

Finding Alternate Weights

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

15

User Selection Example● Red betas are included in the model (nonzero), blue betas
are not included in the model (zero)● This example shows replacing feature combinations 2 and3 with feature combination 1

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

16

User Selection Example● Example with four important feature combinations● Optimal betas from Lasso

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

17

User Selection Example● β2 and β3 have nonzero weights, so they are included inthe model

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

18

User Selection Example● β1 and β4 have weights of 0, so they are not included in themodel

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

19

User Selection Example● Say the user wants to make sure β1 is included in themodel● Need to find a new optimal nonzero β1

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

20

Option 1 - Replace β1 with β2● Find new optimal value of β1

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

21

Option 1 - Replace β1 with β2● Find new optimal value of β1● Set β2 to 0

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

22

Option 1 - Replace β1 with β2● Find new optimal value of β1● Set β2 to 0
● Keep β3 and β4 the same

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

23

Option 2 - Replace β1 with β3● Find new optimal value of β1● Set β3 to 0
● Keep β2 and β4 the same

Satoshi Hara and Takanori Maehara. Finding alternate features in lasso. arXiv preprint arXiv:1611.05940, 2016.

24

Choosing the Best New Solution● Compare the objective values of the new solutions to theoriginal β*25Improvement 2:More Result Options

26

Additional Models to SelectWe can improve the convincingness of our model results byproviding other models to compare, such as:●●●●●●Logistic regression
PerceptronDecision Trees
Random ForestSupport Vector Machines (SVM)
Gaussian Naive Bayes
27

Reasons for selecting six models
・Hello Wide Learning targets binary classification problems.

・Six models are well-known methods in the field ofclassification.

・Six models can be executed using sklearn.
28

Logistic Regression

Perceptron● A statistical model primarily used
for binary classification● it calculates using a linear
prediction model● The analysis is performed bypassing the values through asigmoid function● A simple linear classification
algorithm● calculates the weighted sum of
input data features and classifies
them into two categories: linear prediction model:sigmoid function

outputinput

29

Decision TreeRandom Forest●●●An analysis that creates a tree
diagram from data
Classifies data based on “yes” or “no”
information●Combines the outputs of multiple
decision trees to produce a single
resultAllows for highly accurate predictions
by obtaining majority voting30

Support Vector Machine (SVM)●●

Well-known algorithm in machinelearning
High generalizability with a small
amount of supervised data

Gaussian Naive Bayes●●●Machine learning algorithm used forbinary classification problems
Assume that the characteristics ofeach class follow a Gaussian
distribution
Predicting new classes of data basedon calculated probabilities by learning
the mean and variance of each classfrom training data31

One of our approach● Add “model selection”○ we thought that this proposal would solve the “variety”
problem required by Fujitsu.

● Furthermore, we added the complexity of the model.
○ Improving the interpretability of the models

32Improvement 3:
Explaining the Models andCalculating Complexity

33

Simple = Better? → Rademacher Complexity● Rademacher complexity is a measure that quantifies thecomplexity of a function set.
● Rademacher complexity can be measured for most predictivemodels such as Lasso, decision trees, and SVM.
● Our goal is to use Rademacher complexity to find models thatare not complex. Simple = Better!

34

……

Original Data Set

Randomly re-labelled……

New Data Set

35

New DataSet

Model Set

Model Set
Training
on DataTransfer ofTraining Data

New datasetwith randomly
re-labelled labelsand original
feature matrix

Set of Untrained
ModelsMake a Set ofTrained Models

36Set ofTrained
ModelsOriginalfeature Matrix

Predict random labels
using the original featurematrix.

We obtained
a matrix ofpredictions
for each
model

Model_1

Model_2

Model_3

Model_4

1

1

1-1

1

1

1

1

1-1

1

1-1-1-1

-1

……

37

……

nσ

X

Let the random labels be the vector σ, the featurematrix of the model be the matrix X, and the number ofrows be n.
38

σT X
n
……

n

Calculate the product of σT and X to determine thepercentage of random labels that could bepredicted.
39Model_1

Model_2

Model_3

Model_4

σ(1)

10.5

1

0.5

σ(2)

0.5

0.5

0.50σ(3)

1

1

1

0.5

σ(4)

0.5

0-0.5

1・・・・・

0.72

0.65

0.32

Generate a random label σmany times and average aseries of calculations.
It is called the empirical
Rademacher complexity.

average

Rademacher

……

0.44

……
40

● By measuring the ability to learn to random labels,
the potential complexity of the model could havebeen measured.

● However, we felt the need to evaluate the model inmore detail and prepared a new metric.
41

Our metric shows how well the model predicts as the randomnessof the labels increases.
This allows us to examine how much more random the model
becomes less predictable.
Slightly random Labels
y1Fairly random Labels
y2・・・

Almost random Labely3

42

y1TX 1ny2TX 2ny3TX 3nFor each label, the same process is used as when the Rademacher complexity iscalculated.
43

……

・・・……

・・・

average……

・・・

averageaverage

The process of determining the label for each certain clutter isrepeated many times and the average is taken.
44The axis is blue in the middle, the labels are completely random.
This graph shows how much the model loses predictive accuracy
for random labels as the randomness of the labels increases.
45

Complexity Summary● The potential complexity of the model was evaluated bylooking at the model's follow-up to random labels by
Rademacher complexity.

● Our new method is a more detailed analysis of
Rademacher complexity, which will lead to the discovery
of a simpler model by referring to the graph.
46

“Hello, New Learning!”
Current User Interface

47

New Interface with Our Algorithms

Back-End

Front-End

Python Flask

React JS48

Demo of New User Interface

49

Demo of New User Interface

50

Demo of New User Interface

51

Demo of New User Interface

52

Demo of New User Interface

53

Demo of New User Interface

54

Demo of New User Interface

55

Demo of New User Interface

56

Conclusion● Our version of Wide Learning considers many different models
while making predictions● The user can decide which model they prefer based on accuracy
and complexity● The user also has the option to select important featurecombinations for the Lasso model● The new user interface incorporates additional explanations toaddress feedback from the pilot survey● A new survey has been sent out to evaluate the new workflow andinterface we developed57

Thank You!

58