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Explainable AI Methods andApplications
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such as symptom-based medical diagnosis. But scientists and researchers created
more complicated algorithms in the race to produce accuracy closer to that of a
person. Applications for decision-making based on neural networks and deep learning are quite elusive and difcult to understand.
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1.1 Need forXAI
To allow trust, XAI is primarily required. To be able to believe the model’s conclusions, it is crucial to explain them. Especially when a decision is taken based on
prediction and there are consequences for the safety of people. Recognizing and
comprehending the bias in these choices is another reason why XAI is required. It
is not surprising that bias can exist in the datasets used in practice, even the ones that
are well established, since it can be observed in many facets of everyday life [3].
Unfairness between discriminatory characteristics like gender or ethnicity can be
caused by bias issues. The latest Netix documentary that focuses on the signicance of unfair machine learning algorithms and their impact on society also looks
into this issue. We can identify and comprehend fairness problems with the aid of
XAI, which will enable us to get rid of them. If you still need persuading, there are
plenty more justications listed in the why of Explainable AI.
1.2 Principles ofXAI
To further explain what XAI is, the National Institute of Standards (NIST), a division of the U.S.Department of Commerce, provides four principles of explainable
articial intelligence [4]:
• Each output should be supported by evidence, logic, or other justication offered
by an AI system.
• An AI system should provide its users with clear explanations.
• The process of the AI system should be accurately reected in the explanation.
• AI should only function in the circumstances for which it was intended and
should refrain from producing results when it is uncertain.
2 XAI Methods/Techniques
The term “Explainable AI” encompasses several methodologies and procedures that
aid in elucidating the decision-making process of a given AI model. This emerging
eld of articial intelligence has demonstrated signicant promise, as seen by the
continual development of increasingly advanced methodologies on an annual basis.
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S. Mohanthy et al.
Fig. 1 Overview of methods/techniques in XAI
Several well-known explainable articial intelligence (XAI) algorithms include
SHAP (SHapley Additive Explanations), LRP, DeepLIFT, CXplain, and LIME as
shown in Fig.1.
2.1 Layer-Wise Relevance Propagation (LRP)
Layer-wise Relevance Propagation (LRP) is a propagation-based explainable technique, needs access to the model’s internals (topology, weights, activations, etc.).
However, LRP is able to simplify the model as a result of this extra information,
which helps to solve the explanation issue more effectively [5]. However, LRP is
able to simplify the model as a result of this extra information, which helps to solve
the explanation issue more effectively. More specically, LRP uses the network
structure to redistribute the explanatory factors (known as relevance R) layer by
layer, starting from the model’s output, onto the input variables, rather than explaining the prediction of a deep neural network in one step as model agnostic methods
Explainable AI Methods andApplications
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(e.g., pixels) would. Because it only applies to two neighboring levels, each redistribution can be considered as the resolution of a simple explanation problem (see
the interpretation of LRP as deep Taylor decomposition).
LRP is a well-liked explanation technique that has been used in a variety of
elds, such as computer vision, natural language processing, EEG analysis, and
weather, among others. High computational efciency, theoretical support that
makes it reliable, a robust explanation technique, a long history, and high popularity
are the primary benets of LRP.Restricted exibility is the cost of the benets, for
example, novel model architectures may necessitate a careful adaptation of the
redistribution rules currently in use.
LRP calculates relevance values for all of the neural network’s parts, such as the
weights, biases, and individual neurons, as well as the input variables, to trim and
quantify the neural model in the best way possible. The concept is straightforward:
by simply removing the irrelevant components from the neural network, we can
increase coding efciency and accelerate processing because LRP explanations
inform us which neural network components are pertinent.
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2.2 Local Interpretable Model Agnostic Explanations (LIME)
The LIME method is very exible and broadly applicable because it doesn’t require
any knowledge of the internals of the model, such as the topology, learned parameters (weights, biases), or activation values in the case of neural networks.
The primary goal of LIME is to clarify a complex model’s [Math Processing
Error] prediction. LIME is also frequently known as the surrogate-based explanation method. It is a simple and easy-to-understand surrogate paradigm [6]. As a
result, LIME explains the forecasts of a surrogate model instead of the target model
itself, which is a local approximation of the target model. Although extensive,
LIME’s central concept is actually quite clear-cut and straightforward. Let’s look at
the word itself and what it means.
• Model agnosticism: It is a property of LIME that enables it to provide explana-
tions for any specic supervised learning model by considering each one as a
distinct “black box.” This means that almost any paradigm that is used today can
be handled by LIME.
• Local explanations: LIME provides explanations that are locally accurate within
the vicinity or surroundings of the data or sample being explained.
LIME has been used in a variety of application domains, proving the usefulness
of this technique regardless of the model. It is apparent that LIME’s use of a surrogate model only serves to partially resolve the explanation issue. As a result, the
surrogate t’s quality, which may call for dense sampling and consequently raise
computational costs, largely determines how well the theory ts the data.
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S. Mohanthy et al.
2.3 Counterfactual Method
Utilizing the counterfactual impact evaluation approach, ascertain the proportion of
the genuine improvement that has been observed that can be attributable to the intervention’s impacts (such as a rise in income). Since such improvement may happen
as a result of other causes, such as general economic growth, in addition to the
intervention, Counterfactual arguments attempt to justify the model using the following short and concise claim:
Y would not have happened if X hadn’t happened.
Here, we can consider X to be a data feature and Y to be the result or a predicted
number for a specic instance. Counterfactuals do not necessarily have to be novel
combinations of feature values, unlike prototypes, a different technique under the
category of example-based explanations.
Numerous studies have been conducted on counterfactuals across a variety of
disciplines, particularly logic, statistics, and cognitive science. For XAI, hypotheticals fall under the category of example-based methods. They are founded on methods that gure out what adjustments should be made to the instance data point in
order to alter its prediction to the desired result. The characteristics of an effective
counterfactual are proximity, plausibility, sparsity, diversity, and feasibility. A collection of six distinct categories that represent the “master theoretical algorithm”
from which each algorithm was derived. Instance-centric techniques, constraintcentric approaches, genetic-centric approaches, regression-centric approaches,
game theory-centric approaches, case-based reasoning approaches, and networkcentric approaches are some of the groupings that make up this list.
2.4 SHapley Additive Explanations (SHAP)
A well-known Explainable AI (XAI) system called SHapley Additive Explanation
(SHAP) can offer model-independent local explainability for datasets that are tabular, visual, and text-based. Shapley values are the foundation of SHAP, this idea is
frequently employed in game theory. A method of explanation known as the SHAP
(SHapley Additive Explanations) employs the Shapley values from coalitional game
theory to fairly distribute the reward among players when their contributions are not
equal. Shapley values are an idea from game theory and economics that describe a
way to fairly distribute a game’s prize money among a group of players [6].
Four fairness principles are used by SHAP to equally distribute rewards among
players in cooperative games: (1) Additivity, which mandates that factors must add
up to the game’s outcome; (2) Symmetry, which prohibits a player from receiving a
lower reward for providing more to the game; (3) Efciency states that the forecast
must fairly allocate the value of the feature; and (4) Dummy states that a feature that
does not impact the game should not be taken into account.
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2.5 Generalized Additive Model (GAM)
A generalized additive model (GAM) in statistics is a generalized linear model in
which the linear predictor depends linearly on some predictor variables’ unknown
smooth functions; the emphasis is on drawing conclusions about these smooth functions [6]. Trevor Hastie and Robert Tibshirani created GAMs in the beginning to
combine additive models’ characteristics with those of generalized linear models.
The model links some predictor factors, xi, to a univariate response variable, Y. The
expected value of Y is connected to the predictor variables via a structure, and an
exponential family distribution is dened for Y (for example, the normal, binomial,
or Poisson distributions) along with a link function g (for example, the identity or
log functions).
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3 Example Use Cases forXAI
3.1 Use Case 1: Building aModel Electronic Medical
Record (EMR)
One of the most popular ML use cases in healthcare is feeding model data from
Electronic Medical Records (EMRs) to generate predictions about a patient’s health
outcomes or warn doctors of probable issues. This use case is an excellent way to
show the value of XAI.If the model doesn’t explain why it thinks a patient is more
likely to experience troubles or what those concerns might be, doctors may need to
conduct multiple tests to determine a patient’s diagnosis [7].
An occasional false positive is found. A model can use XAI to explain why it
made a mistaken assumption. Doctors may expedite the diagnosis process in this
instance with the use of an added explainability framework, and the health system
would see a shorter path to treatment. There are many additional applications and
uses available, and this is just a one-use case that the healthcare business can benet from.
3.2 Use Case 2: Healthcare Helps toBuild User Trust—Even
During Life-and-Death Decision
Signicant AI recommendations like hospital stays or surgical treatments need to be
supported with evidence that providers and patients can understand. Doctors,
patients, and other stakeholders can more easily dispute the validity of a proposal by
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understanding the reasoning behind it, thanks to XAI’s interpretable explanations in
natural language or other simple representations.
AI is employed by healthcare professionals to expedite and enhance a variety of
functions, including risk management, decision-making, and even diagnosis, by
scanning medical pictures to nd anomalies and patterns that are invisible to the
human eye. Although AI has become a vital tool for many healthcare professionals,
it is frequently difcult to understand, which frustrates both patients and providers
when making important decisions.
In fact, it is practically hard for a doctor to conrm a diagnosis produced by a
complex deep learning system if they are unaware of the context of the diagnosis,
such as when validating suspicious masses found in medical pictures like MRIs and
CT scans. Since the FDA is in charge of authorizing and evaluating AI models used
in the healthcare industry, Birkeneder observes that this uncertainty is also a persistent problem for the FDA.Doctors must independently conrm the recommendations made by AI systems, according to an FDA draught advisory from September
2019, otherwise these systems risk being reclassied as medical devices, which
have stricter compliance requirements.
S. Mohanthy et al.
3.3 Use Case 3: Explaining Text Data forNatural Language
Processing (NLP) Tasks
NLP operations are supported by XAI.Let’s take a look at a sentiment classication
assignment on the IMDb dataset where the objective is to foretell whether a user
review is favorable or unfavorable. For this classication challenge, PyTorch is used
to train a text CNN model. XAI is then used to generate explanations for each prediction based on test instances. If “preprocess” is the processing function used to
transform raw texts into model inputs, then we want to analyze word/token signicance and produce hypothetical examples [7]. The ndings of the explainers LIME,
Integrated Gradients, and Polyjuice are depicted in Fig. 2. Clearly, LIME and
Integrated Gradients demonstrate that the word “great” has the highest word/token
importance score, suggesting that the statement “What a great movie! if you have no
taste.” is categorized as “positive” because it includes the word “great.” For this test
sentence, the counterfactual method produces a number of counterfactual examples,
such as “what a terrible movie! if you have no taste,” which aids in our understanding of the model’s behavior.
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Fig. 2 Results of NLP task
4 Case Study onXAI inHealthcare
Most articial intelligence models are assumed to be more accurate as they become
more sophisticated, which implies that better prediction performance necessitates a
smart black box. This is frequently not the case, even when the facts are arranged
and accurately depicted in the context of intrinsically pertinent qualities. After data
preprocessing, the productivity of more complex classiers (such as deep neural
networks, boosted decision trees, and random forests) and relatively simpler classiers might occasionally be equal when dealing with structured data that contains
signicant characteristics. There are not many distinctions between approaches
when it comes to data science problems, where structured data with pertinent features is produced as part of the information science process. The data analyst uses a
standardized procedure to analyze the data. This section presents three explain ability case studies to help readers understand explain ability from a healthcare
standpoint.
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S. Mohanthy et al.
4.1 Explainable AI forEarlier Warning Score (XAI-EWS)
The innovation of reasonable simulated intelligence for before advance notice score
(XAI-EWS) [8] comprises serious areas of strength for a compelling man-made
consciousness model for determining basic infections utilizing electronic medical
services-related data. The expectations set out can be directly visually claried
using the XAI-EWS model. The XAI-EWS considered how to assess model translations from two angles: an individual-level angle and a population-level angle.
Separately, the XAI-EWS is empowered to decide in the explanation section which
clinical elements were essential for a given estimate at a specic time [7]. Doctors
typically observe either a high EWS or an increase in EWS in modern clinical practice. However, when the clinician is aware of the specic clinical factors that contributed to the elevated EWS or change in EWS, the associated targeted treatment
for the potential underlying sickness takes place. One of the main sources of inspiration for articial intelligence-driven EWS frameworks is shown in Fig.3 [8]. This
is one of the essential inspirations for man-made intelligence fueled EWS frameworks to excuse such Fig.3 , which addresses the XAI-based EWS model utilizing
brain network models to conjecture the ailment.
In the ongoing contextual analysis, the prior advance notice scores are basically
reliant upon the probabilistic measures related with the SoftMax layer of the proposed model. The probabilities are extremely huge in the tting proportion of the
advance notice score. The straightforwardness in the probabilistic appraisal would
EWS-Module
EHS
XAI-EWS-Module
Fig. 3 Picture addressing the brain network-based EWS-XAI in expectation models
Outcome
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assist the model with being more obvious in the assessment cycle. The major idea
driving bunch handling is to direct appropriation for each secret layer that helps
with expanding the learning pace of the model. Moreover, the clump handling is
performed with each convolutional layer of the evaluation model. Each secret layer’s component circulation continually refreshes because of boundary changes, and
the dissemination logically refreshes the enactment capabilities [8].
Cluster handling would support the uctuation of result include inclinations
since yield highlight dispersions for the most part have higher slopes. Additionally,
while the learning pace of the model is quicker than the preparation period of the
model, it would be a lot quicker with insignicant exertion. The accompanying
circumstance does not completely determine the recipe.
From condition, the variable signies the result of the past layer, where the result
of each layer would yield an ordinary dispersion with a uctuation of 1 and a mean
of 0. To accomplish the nonlinearity in the clump handling, in regards to two extra
boundaries, scale and shift, the resultant result is displayed in the accompanying
equation, where the factors mean the scale and shift, separately, which would yield
the nonlinearity of the model. They would, with a superior nonlinearity, yield an
improved result. The cross still up in the air by the SoftMax layer capability. The
exhibition of the XAI-EWS is being surveyed through 95% of the certainty stretch,
and the cross-approval of the model is being introduced in Table1 for both Region
Under the Recipient Working Trademark Bend (AUROCC) and Region Under the
Accuracy Review Bend (AUPRC) for the three sicknesses like sepsis, intense kidney injury, and lung injury.
It very well may be seen in Table 1 [8] that the presentation of the XAI-EWS
model is sensibly great contrasted with that of the other customary models. XAIEWS exhibits solid expectation exactness, permitting doctors to legitimize the forecasts by distinguishing basic information. The component designing assignments
like the element determination and the clump handling are brought under the logical
ideas that make the model’s forecasts more obvious and genuine. The exhibition of
the XAI-driven model is comparable to the ordinary models, and it very well may
Table 1 Addressing the
cross-approval of the
XAI-EWS models
Mechanisms Cross-validation
AUROCC 0.92
0.80
0.88
0.79
0.90
AUPRC 0.43
0.08
0.22
0.14
0.23
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be seen from the table over that the presentation of the XAI-driven models is far
superior to the customary black-box models.
S. Mohanthy et al.
5 Applications ofExplainable AI
5.1 Healthcare
One of the research areas of XAI with the most activity is healthcare. Clinical
decision- making, disease diagnosis, and recommendation-based healthcare are all
topics of research in this area. In the healthcare industry, doctors with little technological expertise are the main consumers of AI-based technologies. In addition, they
frequently hold their own viewpoints with regard to diagnosing diseases and making therapeutic decisions. Hence, the explanation requested by doctors should have
enough visual and written information as well as pertinent contextual references. A
communicative and interactive user interface should be included in healthcarebased products like tness apps and dietary advice [9].
5.2 Media andEntertainment
Systems for recommending music, movies, works of art for museums and websites,
news articles, and arcade gaming systems are all included in this research eld.
Users of both music and movie recommendations desire personalized suggestions
presented in different explanation styles, and they need explanations that include the
specics of the personalized recommendations, as well as information about the
personal data used [10]. The volume of information being given also raises concerns
from users, who worry that it may lead to cognitive overload.
5.3 Education
Intelligent tutoring systems, systems that help universities decide which students to
admit, and systems that estimate grades are all included in the education area.
Explanations increase the usability of the system, according to studies on intelligent
tutoring systems [11]. The explanations should also include information on the
behavior and operation of the system as well as the reasoning behind certain
decision- making activities, such as admission decision-making. Users frequently
claim that humans, not machines that just use algorithms, should determine admission decisions.
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