g-RIPS Sendai 2023
Fujitsu Group

Final Report

Authors:
Lily Ge1
Yuki Kimura2
Takeshi Nakashima2
Molly Noel3

Mentors:
Jesica Bauer+Dr. Hiroyuki Higuchi*

1

Northwestern University
Musashino University
3
Rensselaer Polytechnic Institute+Academic Mentor, Rensselaer Polytechnic Institute*
Industrial Mentor, Fujitsu Limited
2

Abstract
As Artificial Intelligence becomes more powerful, the need for clear reasoning behind important AIsupported decisions also grows rapidly. In an effort to reduce the gap between humans and AI, Fujitsu
created Wide Learning, an explainable AI tool used for binary classification. Although it provides stepby-step information to help the users understand, initial evidence suggested that gaps remain and thepresentation of information was not always intuitive. We propose several improvements, based on thepilot survey, to the existing Wide Learning tool that are designed to increase its convincingness, variety,and discoverability. To improve discoverability, we add explanations to the tool based on feedback froma pilot survey. For variety, we incorporate different types of models and present them to the user, which
also supports the idea of the hypothesis-driven approach to explainability AI. To increase convincingness,
we incorporate user feature selection and present the user with information about the complexity of eachof the proposed models. These improvements are all incorporated into a new interactive user interface.

August 8, 2023

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g-RIPS Sendai 2023, Fujitsu Group

Contents
1 Introduction

3

2 Literature Review

3

3 Background
3.1 Wide Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Lasso Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3
4
4
5

4 Pilot Survey
4.1 Survey Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Survey Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5565 Increasing Variety: More Modeling Options
5.1 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4 Support Vector Machine (SVM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5 Gaussian Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7
7
8
8
8
8

6 Increasing Convincingness: Implementing Feature Selection

8

7 Increasing Convincingness: Measuring Model Complexity
7.1 Rademacher Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Verification of Model Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9
10
10

8 Increasing Discoverability: Interface Improvements

12

9 New Wide Learning Tool

13

10 New Interface Survey
10.1 Survey Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14
14

11 Discussion and Future Work

14

12 Conclusion

15

A Pilot Survey Visualization

18

B Our New Interface Workflow

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g-RIPS Sendai 2023, Fujitsu Group

Table of Variables and Parameters
Variable
n
p
X
X
y
Y
β
β∗
f
ρ
σ
r

Definition
number of rows in a data set
number of columns, or features, in a data set
matrix of training dataa subset of the training data, X
classification labels for the training data in X
a subset of the classification labels for the training data in X
parameter to optimize in our objection function, e.g. L(β) for Lasso
optimal solution to the objective functiona loss function, e.g. f (X, β, y) as the logistic loss function for Lasso
hyperparameter to control sparsity of β in Lasso
the sigmoid function
an independent random variable from the Rademacher distributionβ̃β (i→j)G
Sweights in the feature selection optimization problem
new weights after performing feature selection (replacing feature i with feature j)
G ⊂ {f : X → Y }S = {x1 , ...., x][n } ⊂ Xn
1∑
R̂S (G) = Eσ supσi g(xi ) .
g∈G n i=1

R̂S (G)Rn (G)
Y′yi′mzi

Rn (G) = ES∼D [R̂S (G)].
Y′ ⊆ Yyi′ ∈ Y ′′}|m ={|{y1′ , ..., ym−yi (yi ∈ Y ′ )
zi =
yi
(y
/ Y ′)[i ∈n∑ĈS,m (G)ĈS,m (G) = Ez supg∈G n1

Cn,m (G)Cn,m (G) = ES∼D [ĈS (G)].

]zi g(xi ) .

i=1

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g-RIPS Sendai 2023, Fujitsu Group

Introduction

Artificial Intelligence (AI) is capable of performing powerful tasks and has become prevalent in many
domains (e.g., [9], [10], [11]). However, its implementation in some fields, such as finance and medicine, islimited due to a lack of user understanding and confidence in the construction of the model and the calculationof the final results. If a model lacks explainability, the users are left with no choice but to trust that thealgorithm’s output is correct—in other words, they are expected to rely on “black-boxes” for making decisions[12]. To address this issue, researchers in AI have teamed up with other fields such as Human-Computer
Interaction to work toward the goal of better human-AI collaborations and investigating Explainable AI(XAI) [1].
One existing approach in XAI is Wide Learning developed by Fujitsu. By incorporating the process of
discovery science, Wide Learning creates a more shallow neural network and provides users with step-by-step
explanations [13]. When investigating this tool, we found that the explanations from Wide Learning are notalways the most intuitive to understand. Additionally, we found that aspects that were more fully explained
existed outside of the tool in areas which were more diﬀicult to discover.
In this project, we aim to start with what Fujitsu has accomplished and further improve the explainability aspect of the Wide Learning tool by increasing the convincingness, variety, and discoverability of theexisting tool. To do this, we identified and incorporated explainability techniques from literature (Section 2),
investigated the existing tool and identified if it utilized these techniques (Section 3), created a pilot survey
to curate information on the existing tool (Section 4), implemented new models to be used in the back-endof the Wide Learning interface to provide more variety to the users (Section 5), incorporated user feature
selection into one of these models (Section 6), developed a new complexity measure to quantify a model’sunderstandability to new users (Section 7), and combined all these results into a new user interface which
we believe will be more beneficial to users of the Wide Learning Tool (Section 8).

2

Literature Review

To achieve our goal of developing an interface that supports the interpretability of AI models, we first
discuss relevant works in the field of XAI.
Prior work has reviewed multiple goals for XAI, such as trustworthiness, causality, transferability, informativeness, confidence, fairness, accessibility, interactivity, and privacy awareness [5]. There also exist
different explainability approaches. For instance, a review by Islam et al. discussed categories of explainability methods: intrinsically interpretable, model-agnostic, and example-based [14]. Intrinsically interpretable
methods include linear / logistic regressions, decision trees; they are considered to not be “black box” models
and are relatively more intuitive [14]. Model-agnostic methods (e.g., feature selection visualizations) are
separated from the actual model itself and could be applicable across different models [14]. Example-based
explanation methods provide explanations using selected data sets that can help explain the behavior of
the model, and relevant methods include counterfactual, adversarial, prototypes, influential instances, and knearest neighbors model [14]. Broadly, explainability techniques can be grouped into recommendation-driven
or hypothesis-driven approaches, which were discussed by Miller [23]. Miller argued that hypothesis-driven
approaches can work better due to its alignment with human cognitive decision-making processes [23]. Allof these approaches are working toward the goal of improving interpretability of AI models, which ultimately improves the overall usability of models in support of decision-making. We design and implementa hypothesis-driven explainability tool that builds off an existing tool (see Section 3.1), and is informed byexisting approaches outlined in literature.

3

BackgroundIn this section, we discuss the necessary background for our new Wide Learning tool. This includes
the existing Wide Learning tool developed by Fujitsu, as well as the mathematical background for logistic
regression and the Lasso method which are currently used in the existing back-end.

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3.1

g-RIPS Sendai 2023, Fujitsu Group

Wide Learning

Wide Learning, a tool originally developed by Fujitsu in 2018, incorporates discovery science and fastenumeration techniques to create a binary classification tool which explains the process of how the model
works [13]. The Wide Learning tool allows the user to explore the process of learning from the training data
and using the model on the test data. The process is broken into the following 5-step process:1. The user is prompted to input a training data set and able to ensure that it was uploaded correctly.
2. Wide Learning analyzes the provided features and generates combinations of features which it determines are important to classification.
3. The user is presented with the trained model as well as the learned results and weights assigned to thefeatures from Step 2.
4. The user is prompted to input a testing data set for use in the model and given the opportunity toensure that it was uploaded correctly.
5. Wide Learning classifies the data set and provides some visuals to justify the final classification result.
Throughout the tool, there are explanatory techniques such as descriptive text, color-coded visualizations,and interactive features which allow users to explore the data and the classifications. However, our initial
exploration identified certain aspects of the tool that may need more granular explanations for users from amore general audience. This may entail explicitly explaining how the combinations were chosen as importantand the weights for the important combinations.
As our group was trained in the tool by Fujitsu Limited, we wanted to ensure that we had a more accurate
reflection of how an unfamiliar user may interpret the interface. We decided to generate a pilot survey to
distribute to other members in the Graduate-level Research in Industrial Projects for Students (g-RIPS), as
their knowledge of mathematics would most likely reflect that of the typical user of the tool. We discuss thissurvey in more detail in Section 4.
3.2

Logistic Regression

The existing Wide Learning tool utilizes logistic regression as part of its classification technique, which isa statistical model primarily used for binary classification problems. That is,y=n∑w i xi + b

i = 1, 2, ..., n

(3.1)

i=1

Then, it transforms the output to a classification using the sigmoid function
σ(y) =

1
1 + exp(−y)(3.2)

where x is an element from the input data X, w is the the weights of the features, and b is the bias term.
A value is classified with class 1 if the transformed value from the sigmoid function is greater than 0.5, andclass 0 if it is less than or equal to 0.5.
By minimizing the loss function on the training data set, logistic regression learns the model’s parameters
by making predictions based on the input features, which in turn helps determine the feature importance. It
utilizes ℓ2 -regularization to encourage sparsity of the model weights and calculates a weighted sum of input
data features using a linear prediction model. ℓ2 -regularization adds the square of the absolute value of thecoeﬀicient to the objective function as a regularization term to control the coeﬀicients from becoming toolarge [19].

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3.3

g-RIPS Sendai 2023, Fujitsu Group

Lasso Method

Tibshirani (1996) proposed the Least Absolute Shrinkage and Selection Operator (Lasso) as a way toselect important model features using the following optimization problem [6]:arg min L(β) := f (X, β, y) + ρ∥β∥1

(3.3)

β

where f is a loss function, X ∈ Rn×p is the input training data, y ∈ Rn is the vector of true outputs for the
training data, ρ is a hyperparameter that determines the weight of the regularization term, and β ∈ Rp+1 are
the model parameters (one weight for each feature, p, and an intercept β0 ). Here n is the number of training
samples and p is the number of features. In this objective function, the first term represents the training loss
based on the model prediction. The second term in the objective function is the regularization term, which
encourages β to be sparse. The nonzero elements of the optimal β found by solving this Lasso optimization
problem represent the weights on the important features that are extracted from the model [2].
We can equivalently pose the formulation by dividing the results by ρ to more clearly see the sparsity ofβ. Let C = 1/ρ. Therefore, a smaller value of C puts less weight on the logistic loss term of the function
and a higher relative weight on the regularization term, which encourages β to be more sparse. For our
implementation of Lasso, we choose C in order to keep the number of nonzero weights less than 100. In
practice, we initialize the problem with C = 18. If that value results in a number of nonzero weights greaterthan 100, C is multiplied by 0.9 until the number of nonzero weights until the sparsity is less than or equal
to 100. The Lasso objective function used in our implementation is defined asarg min L(β) := C[f (X, β, y)] + ∥β∥1

(3.4)

β

For the case of Lasso logistic regression, the loss function f is defined as follows. Here, let X[i, :] is theith row of X, and X[i, j] is the (i, j) entry of X. Then we can formulate our loss function asf (X, β, y) :=n∑−[yi log[σ(X[i, :], β)] + (1 − yi ) log[1 − σ(X[i, :], β)]]

(3.5)

i=1

where σ is the sigmoid function that calculates the probability that a given data point has a label of 1.
This function is also used to make predictions on testing data points after the model has been trained. A
probability greater than 0.5 leads to the testing data point to be classified with a label of 1, otherwise it isclassified as 0. We can more explicitly state this asσ(X[i, :], β) = P (yi = 1|X[i, :], β) =1
1 + exp(−[β0 + β1 X[i, 1] + β2 X[i, 2] + · · · + βn X[i, p]])(3.6)

By utilizing logistic regression and encouraging sparsity, we reduce the number of parameters from theinitial problem and identify features which have a higher affect on the classification. While the underlying
mathematics are more complicated, the results from the process are easier to understand for the user. Having
less features naturally makes the problem easier to understand, as it lowers the dimensions of the problem.
Additionally, the features which continue to exist within the problem due to their non-zero β should prove
to be useful identifiers for prediction of future data sets even without the use of the tool.

4

Pilot Survey

In order to improve the Wide Learning tool, we first needed to better understand which aspects of theexisting tool may benefit from additional explanations and which parts should be maintained. Therefore, we
designed a pilot survey to collect data from people who have not yet interacted with the Wide Learning tool
as extensively as our own team members.
4.1

Survey Structure

The survey was designed to take no more than 15 minutes in total and included screenshots from theexisting tool to request feedback from the participants. Screenshots of the existing tool and the survey canbe found in Appendix A. In general, the survey contained the following:5

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g-RIPS Sendai 2023, Fujitsu Group• Steps 1 (training) and 4 (testing) of the Wide Learning interface with the relevant training and testing data set already imported. We used the “Animals (mammals) classification” data set which wasprovided as an example for the tool.
• Results from Steps 2, 3, and 5 of the Wide Learning tool followed by open-ended questions to elicit
people’s thoughts on the interface. Participants were then asked to rate how confident they were in
their responses on a scale from one to five.
• Questions to identify if there are any confusions that need to be clarified in the existing interface.
• After people were asked to enter their responses, they were directed to the next page which included
a summary of what the screenshot was showing and a question to ask them to describe whether theirunderstanding aligned with our summary; if not, they were asked to respond with what they thought
would have been helpful for them to understand.
4.2

Survey Results

We sent the original pilot survey in the #students-grips2023 Slack channel with 16 potential participants
and received 10 responses. These results were used to further inform how to design our interface to help
improve interpretability. While overall participants seemed to have a general understanding of what the Wide
Learning tool was displaying, there were several areas in the user interface that they thought were confusing.
Much of the confusion was stemmed from the “important feature combinations” which were displayed in
Step 2 of the Wide Learning process. A screenshot of the tool at the time of writing is shown in Figure 1.

Figure 1: A screenshot of Step 2 of the Wide Learning tool at the time of writing this document.
The first area of confusion regarded the table headings: Combinations of important items, Combination Length, occurrences of POS, and occurrences of NEG. Some participants thought thatthe occurrence of POS column meant the number of training samples that had that combination, rather
than the number of training samples that had that combination that belonged to the determined “positive”
class. Additionally, some survey respondents did not understand the formatting for the Combinations ofimportant items column. For example, Teeth_Has ∧ Breathes using lungs_Yes.
The second major area of confusion regarded Step 3 of the Wide Learning tool (shown in Figure 2).

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Figure 2: A screenshot of Step 3 of the Wide Learning tool at the time of writing this document.
This table displayed a column importance (weight) but did not explain how this weight was calculated
or what it correlated to even when users interacted with the “[Intermediate users] Check the evaluation valuesof the learning results here” dialog box.
With the results of this survey in mind, we wanted to ensure our future interface provides more context
for these areas of confusion. Additionally, we hope to expand the interface to provide more convincingness,variety, and discoverability to further enhance the user experience. The following sections discuss individual
aspects which we have developed to increase the value of the Wide Learning tool Section 8 showing how eachpiece was implemented into a cohesive product.

5

Increasing Variety: More Modeling Options

It is common for an explanation for a judgement to not always be unique. In fact, allowing a user tosee multiple perspectives can help increase their trust in the final presented result. The existing interface
for Wide Learning only presents the user with one model, meaning the user is not presented any variety.
Therefore, we hoped to implement some new models to execute on the back-end to provide the user withmore options to get their results and additionally ensure that they are always getting the most accurate
prediction.
In this section, we describe additional models that are incorporated in our proposed interface with theintention to be to provide the best of these options to the user and allow them to investigate the results of
the other models. Due to time constraits these models were all implemented using Python’s scikit-learn
library unless otherwise noted [15].
5.1

Decision Tree

Decision Trees are one of the machine learning algorithms commonly used in supervised learning. They
employ a tree-like branching structure to classify or regress data. At each stage, the data is split based on

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specific features, forming new groups or child nodes. Eventually, the data reaches leaf nodes where it isassigned to specific classes, thus performing the classification [16]. The advantage of decision trees lies intheir interpretability, as the tree’s structure allows for the visual inspection of feature importance. Featureimportance is computed using the Mean Decrease Impurity (MDI) method, which evaluates how much eachfeature can separate the data by considering the impurity within nodes . When constructing the decision
tree, the importance of each feature is estimated based on how much it reduces impurity within the nodes.
A higher numerical value indicates a more important feature. However, it might be challenging to determine
whether the importance is positive or negative. Nonetheless, by considering the magnitude of the numericalvalues, one can still understand the relative importance of features .
5.2

Random Forest

Random Forest is a machine learning method that combines multiple decision trees to make predictions.
Ensemble learning, which is used in Random Forest, involves combining multiple machine learning models
to achieve better performance than individual models. When determining feature importance in Random
Forest, the average reduction in impurity calculated from multiple decision trees is used [17].
5.3Perceptron

Perceptron is a simple linear classification algorithm used for 2-class classification problems. Similar tologistic regression, it calculates the weighted sum of input data features and classifies them into two categories
based on whether the value exceeds a certain threshold. However, it is only applicable to linearly separable
data, and the boundary is determined by a straight line. Furthermore, like logistic regression, the perceptron
determines the feature importance by training its model parameters using the training data [20].
5.4

Support Vector Machine (SVM)

Support Vector Machine(SVM) is a machine learning model used for classification problems. SVM aims
to find a hyperplane in the feature space that best separates two classes. The goal is to find a hyperplanethat maximizes the distance (margin) between data points. Additionally, the distance from the data points
within the margin to the hyperplane should also be maximized [18]. By following these steps, we can discover
the hyperplane. In addition, in the linear SVM model of scikit-learn, weights are assigned to each feature
to find a hyperplane that separates the data. The computation of SVM weights is based on the equation of
the separating hyperplane. During the training process of linear SVM, the objective function is minimized
to find the optimal separating hyperplane. In this process, a regularization term is introduced to determine
the weights. This allows us to understand the importance and influence of each feature.
5.5

Gaussian Naive Bayes

Gaussian Naive Bayes is a machine learning algorithm used for binary classification problems. It assumes
that the features of each class follow a Gaussian distribution and uses Bayes’ theorem to calculate theconditional probability. By learning the mean and variance of each class from the training data, it can
predict the class of new data based on the calculated probabilities. However, since it uses a probability
model and it is diﬀicult to directly interpret how specific features influence predictions, it is considered a
black-box model [21]. Therefore, if this model is chosen during model selection, it lacks interpretability, andit would be necessary to add post-hoc explanations. However, at the moment, we have not yet considered an
approach for addressing this aspect.

6

Increasing Convincingness: Implementing Feature Selection

A result which is accepted by the user is considered to be “convincing”. One such way that we can increase
the tool’s convincingness is to allow the user to have input in what features are included in the model. Being
able to directly interact with the model and prioritize information which may be important in practice could
increase this metric.

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g-RIPS Sendai 2023, Fujitsu GroupThe optimal features selected by Lasso might not be the only important features [2]. The paper by Haraand Maehara (2016) introduces a way to replace certain features selected by Lasso with user selected features.
This can increase the trustworthiness of the model, as it allows the user to make sure that the features thatthey believe are important are included in the model [2]. This paper also introduces a way to calculate a score
between two features, to determine if they are similar enough to have one replace the other with a minimal
decrease in accuracy. We apply this technique to the feature selection in the new Wide Learning interface
in order to increase the model’s trustworthiness. The following is a description of the process developed byHara and Maehara (2016) [2] that we apply to our Logistic Lasso model:
The original Lasso model performs feature selection by assigning nonzero weights to some features andzero weights to others. The features that are assigned zero weights are not included in the final model. The
process by Hara and Maehara (2016) allows users to select features that were not included in the originalmodel. Once this feature has been selected, a new nonzero weight is assigned to the feature. To keep thenumber of nonzero weights the same as the original model, when one feature is added to the model by changing
its weight from zero to a new nonzero value, another feature is taken out of the model by reassigning it aweight of zero. The optimal model weights selected by Lasso are denoted by the vector β ∗ ∈ Rp+1 , where pis the number of feature combinations generated by Wide Learning. Note that β0 is the intercept term that
does not correspond to any of the model weights, and is not eligible to be added to the model or taken outof the model.
Say the user decides to select feature j to add to the model, with corresponding weight βj∗ = 0. To
maintain sparsity, let i be an index such that βi∗ ̸= 0; this will be the feature that is removed from the modelby setting βi = 0. Then, define β̃ such that β̃i = 0, β˜j ̸= 0 and β˜k = β ∗ where k ̸= i, j. The new nonzero
value of βj is found by solving the following optimization problem:(i)βj = arg min C[f (X, β̃, y)] + |β˜j |

(6.1)

β˜j

(i)

where βj is the optimal βj for the above problem. This is the same objective function as the original Lasso
problem, however we are only optimizing with respect to βj . This optimization problem is solved using theproximal gradient descent method with a constant step size.
The updated model weights after adding βj to the model and taking βi out of the model are defined as
(i→j)
(i→j)
(i)
(i→j)
(i→j)
β
, where βi= 0, βj= βj , and βk= βk∗ , ∀k ̸= i, j
(i)

We know βj = 0 when C|∇f (X, β̃, y)| ≤ 1 based on the optimality condition defined in [2], where∇f (X, β̃, y) is the gradient of f with respect to βj . Since the goal of this optimization problem is to find a(i)
new optimal nonzero value of βj when βi is set to 0, if this optimal value βj is 0, then βi cannot be replacedwith βj in the model.
(i)
For a given βj to be inserted into the model, any βi ̸= 0 where βj ̸= 0 is a candidate for the feature
combination that can be removed from the model. In order to choose which βi is the best feature to
be removed, we compare the original Lasso objective value for all of the potential new solutions β (i→j) .
Whichever solution leads to an objective value L(β (i→j) ) that is closest to the original objective value L(β ∗ )(i)is the best new vector of weights. If all βj = 0, then βj cannot be inserted into the model. We use thismethod to perform user feature selection for Lasso in our user interface.
The code to perform this user feature selection is implemented in Python. The original Lasso coeﬀicientsβ ∗ are found using the scikit-learn library for Logistic Regression with the L1 regularizer and the liblinear(i)solver [15]. PyTorch is used in our implementation of the proximal gradient descent method used to find βj .

7

Increasing Convincingness: Measuring Model Complexity

In addition to allowing user input, convincingness could also be determined by how complicated themodels used are. However, we found that some of the existing metrics for complexity failed to account for
information which we felt was valuable to reflect the user experience. Therefore, we generated a new metric
which reflects the complexity of the model to the user and which allows them to select a model which may
be more applicable and understandable for their usage.

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7.1

g-RIPS Sendai 2023, Fujitsu Group

Rademacher Complexity

We define a hypothesis set from the collection of models discussed in Section 5 and Section 6. Thishypothesis set can respond to any labeling, which means that it is possible for additional noise to also belearned. We examine the complexity of the model using the concept as it is treated in machine learning
using Rademacher complexity. That is, a model which is able to easily follow random labels is more complex
and therefore sacrifices its explanatory power. As Wide Learning does binary classification, we redefine
Rademacher complexity by consulting [22].
Let the input space X be mapped to the label space Y such that Y = {−1, 1}. The set of classifiers that
map the input space to our label space is denoted by G ⊂ {f : X → Y }. The set of input points is denoted by
X = {x1 , . . . , xn } ⊆ X and the corresponding labels are {y1 , . . . yn } ⊆ Y , where yi ∈ Y ∀i where n represents
the total number of data points or samples in our data set. Let {r1 , . . . , rn } be independent random variablesfrom the Rademacher distribution. That is, ri is either −1 or 1 with equal probability.
Then, the empirical Rademacher complexity is defined as[]n
1∑R̂X (G) = Er supri g(xi )(7.1)
g∈G n i=1
which indicates an intuitive explanation. When ri g(xi ) = −1, meaning that ri is 1 and g(xi ) is −1, or ri is−1 and g(xi ) is 1, g(xi ) failed to make a correct prediction. Conversely, if ri g(xi ) = 1, it means that g(xi )successfully carried out the forecast. The sum of these tells us how well the model g(xi ) is able to follow therandom labels ri and the randomly labelled xi . The Rademacher complexity is then shown.
However, in proposing this method, we found an interesting result. When the input point X = {x1 , ...., xn }is a random variable following some distribution D, the Rademacher complexity of G can change dependent
on the chosen distribution D. This means that depending on our initialization of our random labels, we could
get a complexity measure which is inaccurate.
Therefore, we can redefine our Rademacher complexity as
Rn (G) = EX ∼D [R̂S (G)](7.2)

The reason why the Rademacher complexity takes the expected value of the empirical Rademacher complexity is that the value of the empirical Rademacher complexity depends on the data. In other words, taking
the expected value of the empirical Rademacher complexity for a set of input points X is the definition of
Rademacher complexity.
7.2

Verification of Model Complexity

As mentioned in the previous section, the Rademacher complexity of our models is dependent on the
distribution chosen for our random labels. This could result in our interface suggesting a model which is only
perceived to be less complex due to a favorable initialization.
Our question is: if two models have the same Rademacher complexity for a given data size, are they
similarly tracked for a given label? When the Rademacher complexity of a model that can follow somerandom noise but not completely random labels is equal to that of a model that cannot follow some random
noise, it seems that they should not be treated as equivalent models.
Therefore, we propose a method to assess the models ability to follow label clutter.
The operation of adding noise to the training data seems to be called ”noise injection”. However, we
couldn’t find a specific name for the method of adding noise to the labels of the training data and measuring
the complexity of the model. We denote this new complexity measure as Ĉ.
′Let Y ′ ⊆ Y such that |{y1′ , ..., ym}| = m with 0 ≤ m ≤ n. For every element yi ∈ Y, we define a newelement zi as{}−yi (yi ∈ Y ′ )
zi =
(7.3)
yi
(yi ∈
/ Y ′)
Then, the definition of complexity Ĉ isĈX ,m (G) = Ez

[

1∑
sup
zi g(xi )g∈G n i=1

10

n

]
(7.4)

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This new measure allows us to arbitrarily select n real labels that a certain data has without duplicationand then invert the labels, i.e. prepare new labels that are -1 times more real labels. The complexity Ĉ is ameasure of the average conformity of the function set G to the labels for such labels. When m = 0 or m = n,
it is exactly the same as or exactly opposite to the actual label. On the other hand, when m = n/2, half ofthe labels are random.
If an input point X of this complexity follows a certain distribution D, C is defined as
Cn,m (G) = EX ∼D [ĈX (G)]

(7.5)

Then, let us compare the correspondence between Rademacher Complexity and Complexity C.
The set of possible values of m is M , i.e., M = {m ∈ Z : 0 ≤ m ≤ n}. The range that the error between
the random label generated from the random variable ri and the real label can take is equal to M . Consider
the probability that the error between a random label and a real label can be k. The probability that any
one random label is equal to the real label is 1/2. The error between the random label and the real label isk, given that the size of the label is n. So, the probability that the error occurs exactly k times in n trials,
i.e., it follows a binomial distribution. We denote such a given probability as P(M =k) . Since the expectedvalue of the binomial distribution can be expressed as n/2, the Rademacher complexity is approximately
Rn (G) = Cn, n2 (G) or more strictlyn∑
Rn (G) =P(M =i) Cn,i (G)i=1

Figure 3 visualizes how much the model is unable to follow a given label as the randomness of the label
increases. Using the traditional definition of Rademacher complexity, the Perceptron and the 2-way decision
tree have a Rademacher complexity of 0.304 and 0.281 respectively. However, from the figure, we see that
the Perceptron is less trackable than the 2-way decision tree as the label clutter increases. In this way, we
believe that this metric may allow us to more consistently find a simple model than the traditional definition.

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Figure 3: The graphs above are for various models, varying the complexity ĈX ,m m from 0 to n in the animal
data set.The flip rate on the x-axis corresponds to n/m. The graph below is a Fourier inverse transform after
removing the high-frequency spectrum obtained by Fourier transform.

8

Increasing Discoverability: Interface Improvements

As discussed in Section 4, the existing interface was confusing to users because of lack of explanation
throughout the tool. While additional explanations existed elsewhere on the website, making it diﬀicult tofind and not accessible while interacting with the tool.
To address these issues identified from the initial pilot survey, we added additional explanations in theforms of paragraphs explaining the underlying models which were being used. Additionally, we added hover
text to the columns to describe exactly what the columns were representing.
We also reformatted the combinations generated in Step 2 so that they are more human-readable. That is,
instead of [feature]_[classification], we did [classification] [feature]. For example, Teeth_Has∧ Breathes using lungs_Yes became "Has Teeth" and "Breathes using lungs".
By making these changes, we believe that users will have an increased discoverability with the tool, as
they are now able investigate without going to external sources. We also have ensured that the informationin the previous sections also follow these new guidelines to continue to be useful for the users.

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9g-RIPS Sendai 2023, Fujitsu GroupNew Wide Learning Tool

We improved the front-end interface of the system that also incorporates user input. We iterate on thedesign of the interface using the data collected from the pilot survey as well as our own experiences.
For the development of the interface, we used programming languages Python and Javascript. The backend of the interface is supported by Flask, and the front-end is supported with React JS. For version control,
we used Github to store the up-to-date version of our code; see the repository here.
The models and data are retrieved from the back-end for the user to interact with. The development of
the new interface was done on a local server. For instance, every time the page loads, the back-end code
will generate the data needed to be displayed to the user, which will be fetched using a ‘GET’ request in the
Application Programming Interfaces (API) call. Once the user inputs a desired feature or uploads a dataset, the API would send a ‘POST’ request to the back-end, which includes the data from user that will getprocessed. The details of the new interface are outlined in Section 9, which are separated into 8 steps (seeAppendix B for demo videos).
The original Wide Learning tool breaks down the process into five steps, as described in Section 3.1. The
technique used from step 1 to step 2 in Wide Learning is highly effective and eﬀicient at identifying important
combinations of features in the data [13]. Therefore we will continue to use this same technique for our new
Wide Learning interface.
The area of the current Wide Learning process that we changed was the process by which weights are
assigned to feature combinations between step 2 and step 3.
The existing Wide Learning tool only presents one model to the user that is used to make predictions on
the test data. However, many types of models exist that can effectively perform this binary classification,
including those described in Section 5. Our new user interface presents the user with different options formodels. Allowing the user to have some choice can increase the trustworthiness of the model [2].
The new interface performs the classification task as follows:
1. The user uploads their training data.
2. Important combinations of features are generated and shown to the user, using the same techniquefrom the original Wide Learning tool. The training data is used to create a binary matrix based onthese important feature combinations.
3. This binary matrix is used to train all of the Lasso, SVM, Decision Tree, Random Forest, Logistic
Regression, Gaussian Naive Bayes, and Perceptron models as discussed in Section 5.
4. Training accuracy and complexity scores are calculated for each model following the metric shown in
Section 7.
5. The user is then shown the model with the highest training accuracy. If multiple models have the same
training accuracy, the one with the lowest complexity score is shown.
6. After seeing this best model, the user is presented with a list of the other models, along with theircorresponding training accuracies and complexity scores. The user can then select one of these models
if they are not satisfied with the original model.
7. If the user selects Lasso, they will also be presented with an option to modify the Lasso model by
selecting feature combinations to include in the model, as described in Section 6.
(a) Once the user selects a feature combination, it is inserted into the model and another feature istaken out of the model.
(b) The updated weights are shown to the user.
(c) At this point, they can either select another feature combination to add to the model, or accept
the current model.
8. Once the user has decided which model they want to use, the testing phase begins. The user uploads
their testing data, then predictions are made based on the model that was selected by the user. The
user will be able to see the predictions for the other types of models as well.
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g-RIPS Sendai 2023, Fujitsu GroupThe videos of the current state of the interface can be found in Appendix B.
We believe that this new interface improves upon the existing Wide Learning tool in all requested aspects
from Fujitsu Limited by improving the tool’s variety, convincingness, and discoverability. However, we needto assure that these metrics were actually met per their guidelines.

10

New Interface Survey

We used our first pilot survey to get feedback on the existing Wide Learning tool. The survey responses
provided valuable information that helped inform the creation of our new Wide Learning user interface. To
evaluate the clarity and explainability of this new Wide Learning workflow and user interface, we designed
a similar pilot survey for the new tool. The new user interface is more interactive and certain aspects havehover explanations. Therefore, we used video clips that demonstrate how the interface works. In the survey,
short video clips are shown for each step that demonstrate what the new Wide Learning tool is doing (seeAppendix B for demo videos). After watching the video for each step in our user interface, the survey
respondent is asked to describe what they think is happening in that step. They are also asked to describe
any aspects that they found to be particularly confusing or clear. Next, they are asked to rate how confident
they are in their description on a scale from 1 to 5. At the end of the survey, they are asked to comment on
the clarity of the interface as a whole. They are also asked to provide any final comments or suggestions.
10.1 Survey Results
The pilot survey for the new user interface described in Section 10 was distributed to several Fujitsu
employees. The survey was anonymized and was expected to take 15 minutes to complete. We received 5
responses. These respondents were already familiar with the existing Wide Learning tool When asked torate their familiarity with Wide Learning on a scale from 1 to 5 (1 being Never Used it Before and 5 being
Expert), two respondents selected 4 and three respondents selected 5. When the respondents were asked todescribe what was happening at each step and to comment on aspects that they found confusing or clear,
the responses varied from person to person. At each step, some people found that it was clear, while others
identified areas that they found confusing. Unlike our original pilot survey, there was much less of a consensus
as to which aspects were clear or confusing. However, these survey results still provide valuable feedback
that can be used to make further improvements to the new user interface. For the last questions, where
respondents were asked to comment on the overall flow of the interface and give any additional feedback.
Overall, the feedback was positive and people said that they were able to understand the flow of the userinterface.

11

Discussion and Future Work

While this project made several improvements to the existing Wide Learning tool, there are still several
directions that could be explored to further improve explainability in aspects such as convincingness, variety,and discoverability. As described in the previous section, the feedback from the new pilot survey can beincorporated into future iterations of the interface. This could include adding additional explanations or
making improvements to the existing ones.
The results from the survey on the new interface suggest that certain parts needed better representations,such as the graphs showing the change in complexity values (Figure 3), even more so for non-domain experts.
Out of the 5 responses we received, 4 respondents expressed some confusion on how to use the graphs, whichsuggest that there could be better ways, perhaps simpler or more scaffolded ways, to present the complexity
data. One future direction of research could be to explore and evaluate different representations to help with
understanding.
In the last step of the new interface, we currently show the predictions from the set of models. Anotherfuture direction would be to show the user a more direct comparison of the prediction results from the different
models for each testing data point. If the user could see that the majority of the models considered madethe same prediction for a given data point, that could increasing the convincingness of those predictions.
While developing and testing our new user interface, we used the datasets provided in the Wide Learning
Website (mainly the animal data for mammal classification). For further development of this tool, more
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g-RIPS Sendai 2023, Fujitsu Groupdatasets should be tested, including datasets not provided by the Wide Learning Website.
As with the nature of development of applications, many iterations are necessary to ensure the quality ofthe tool. We hope our iteration of the interface can serve as a foundation to future iterations of improvements.

12

Conclusion

We have created a new version of the Wide Learning tool based on hypothesis-driven AI. Rather thanpresenting the user with one model to perform classification, we train multiple models to present to the user.
First, we recommend which model is best, and then present the alternative models. We provide the training
accuracy and the Rademacher complexity for each model so the user can make an informed decision. If theuser selects the Lasso model, they are able to select feature combinations that they believe are important toinclude in the model. By giving the user some control in which models are being used, this could increase
their trust in the model [2]. We also include additional explanations in our interface that were informed by
the results of our initial pilot survey, to increase the explainability of the Wide Learning tool.

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g-RIPS Sendai 2023, Fujitsu GroupA Pilot Survey Visualization

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g-RIPS Sendai 2023, Fujitsu GroupB Our New Interface Workflow
Step 1: Upload Training Data
Step 2: Get Important Combinations
Step 3: Learned Weights
Step 4 (part 1): Set of Possible Models
Step 4 (part 2): Set of Possible Models (Results)
Step 5 and 6: User select feature and display weights
Step 7: Upload Testing Data
Step 8: Predictions from the Possible Models

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