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Explainable Artificial Intelligence
- A Beginner's Guide to xAI -

15 min read        December 1, 2021       Article

15 min read
December 1, 2021

A HealthWorksAI Insight by Vimal Gopal

Introduction to Explainable AI – The Future of Analytics

Today’s AI systems are so powerful that they can predict human behavior, but very little data is available to the user about how it arrives at its conclusions. Machine learning delivers impressive power – but this power is opaque. It’s like having a mysterious black box making all your decisions for you without you knowing why.

In most cases, it is a human convention to trust a machine’s decisions only when the machine makes a decision that humans understand. Example: We put our faith in an autopilot system that lands a plane only if we completely understand how its sensors and algorithms work. But what happens when a machine makes a decision that we don’t understand? Are we going to trust that machine blindly?

AI transparency is the most critical issue in AI. Currently, there is no AI transparency. There is only trust that the machine has made the right decision. Explainable AI is about ensuring that trust by making the predictions clear and transparent. It is an emerging technology in Machine Learning (ML) that aims to give humans visibility into how AI systems work and be able to explain why they reached a certain conclusion.

What you'll learn

What is Explainable AI?

The Concept of xAILet’s start with Artificial Intelligence (AI), it is one of the most popular technologies in today’s world. It has rapidly evolved into an industry that has created countless jobs and is expected to bring in $15.7 trillion by 2030. However, the present AI technology is still in its infancy, undeveloped, and has many problems that must be addressed. Majority of the artificial intelligence projects are not “explainable” – they cannot provide key information about why they reached a certain conclusion or what actions they will take in the future. This often makes it difficult to trust the technology. Explainable AI refers to the technology that can provide information to humans about how AI reached its conclusion. The technology is also known as “explainable machine learning”.

Explainable AI systems help businesses better understand the logic behind AI. They are built by using algorithms and mathematical representations, allowing the systems to be taught how to make complex decisions, which can then be examined to determine why they made those decisions. This allows for more meaningful interaction between human beings and artificial intelligence. They can, for example, ask for help when the answers become too complicated for them to deal with; and give a reason for what they do in case they’re unable to explain their actions.

How does Explainable AI work?

Most current AI systems are designed to handle large amounts of data with a high degree of flexibility and have few built-in controls. As a result, they are often programmed with a set of rules or programming instructions that govern their actions, making them hard to interpret.

Because these systems require precise and robust mathematical and logical reasoning, engineers are often forced to code detailed explanations for the decision-making process or leave them to the system to determine. However, if a machine’s actions are written in a way that is unable to explain itself, it cannot learn, adapt, or evolve in the same way as a human being. So how can AI systems be designed in a way that allows them to explain their actions to their designers?

Through Explainable AI, which is a form of artificial intelligence that uses data and  reasoning capabilities to explain its decision-making process. This means that AI is not so much a black box anymore. It does not operate in a way that humans cannot understand. For instance, if you were to ask the AI system, “Why did you decide to do X?” the system would be able to give you a detailed explanation of why it made that decision. It also provides evidence for its outputs, which can help ensure accuracy and transparency in the use of AI systems. However, Just like any other AI system, explainable AI must be trained with large amounts of data on how humans make decisions to provide more accurate explanations.

Example or Use cases of Explainable AI (xAI)

Imagine an autonomous driving car programmed to follow all traffic regulations on the road, like stop signs and red lights. If you are about to turn left onto the road, at this point, the car can make a safe decision to stop, follow the right-hand lane, and turn right. This decision may seem purely mechanical, but it is actually the result of a complex decision tree. This means that the car knows the rules and probabilities of making each of these three decisions. Without some form of explainable AI, it wouldn’t be possible to explain the decision.

Here’s another use case of xAI – Let’s say a patient with leukemia wants to know their risk of recurrence based on factors like family history, cholesterol levels, and diet. A doctor can use an explainable AI system that has all this information (stored in their database) to predict if the patient will get sick again or not (with an explanation to back it up).

At the moment, most of this research is focused on answering why these kinds of decisions are made.

How does HealthWorksAI use Explainable AI in its systems?

HealthworksAI’s xAI Solution is designed for senior health plan leaders and payors to support strategic decision-making. It helps create weights for the various attributes/factors to better understand the correlation between the product and beneficiary preferences. It also provides analytics & insights on the influence of different verticals on enrollment growth. This helps Payors focus attention on any verticals independently or combined – by diving deeper into the respective vertical reports to uncover additional insights.

Our xAI program is an advanced correlation analysis that collects and processes data, generating in-depth market analysis that standard research models do not uncover. The result is actionable intelligence to enable business optimization. Learn more about our xAI solution designed explicitly for healthcare payors.

Why is Explainable AI important?

AI has moved beyond merely digital, becoming an integral part of our society. AI can now understand and interact with us on a variety of levels, including helping to diagnose diseases, predict the weather, perform scientific research, and improve the quality of medical care.

Due to the accuracy and usefulness of AI systems, we have come to rely on AI as a staple part of our modern-day lives. AI systems can help us carry out thousands of tasks every day, from diagnosing illnesses to driving our cars to even cooking dinner. Moreover, AI systems perform these tasks much faster and more accurately than we can by analyzing massive amounts of data.

All this sounds great, but AI systems are still intangible, as their predictions are often incomprehensible to humans. They can never tell us what causes the results that we see.

What are the goals of Explainable AI?

The primary goal is to enable data scientists and AI engineers to get a deeper understanding of the reasoning behind AI predictions. This allows the data scientist to explain to non-experts why a particular prediction was made.

The concept of explainable AI is based on the idea that any AI system is only as good as its ability to explain itself. Think about it, if you’re using AI to make decisions, you want to know why it made those decisions. If you’re using AI to diagnose an illness, you want to know why the AI says that you have a certain illness. And if you’re using AI to control a self-driving car, you want to know what the AI will do in any given situation. If an AI is not explainable, it is not trustworthy, and not useful. We need AI to explain itself so that we can trust it and use it.

What are the principles of Explainable AI?

Explanation: Artificial intelligence systems need to provide evidence, support, or reasoning for every single output! That means that if an AI system is trying to predict the weather at 5 pm, it will have to show you all of the information it used in its prediction.

Meaningful: {Systems should provide explanations that are easy to understand} and/or {it should be useful to complete a specific task}.

Explanation Accuracy: The explanation must clearly show how the system works in order to generate the output.

Knowledge Limits: The system only operates under conditions for which it was designed or when it has reached a sufficient level of certainty in its output.

How can Explainable AI aid the healthcare industry?

The answer to the above question is relatively straightforward, as explained by University of Pennsylvania professor António Damásio. The healthcare industry is in dire need of easy-to-use, robust, accurate, efficient, and accurate algorithms and models for diagnosis, treatment, and disease monitoring. However, their algorithms are often too complex, narrow, and constrained by existing language, data, and computing resources.

As AI systems become increasingly more advanced, it will be critical to ensure that patients and their doctors understand how the AI makes its decisions.

Explainable AI is set to revolutionize the healthcare industry. This is because xAI can not only identify but also explain what it sees. This has the potential to improve diagnosis, streamline operations, and enhance the ability to make informed decisions. In the next five years, AI will be used as a tool to help doctors diagnose and treat patients, as a supplement to a physician’s diagnoses, and as a way to bolster a physician’s confidence.

What are the benefits of xAI?

A framework, or rules-based architecture, in particular, “explainable AI,” is known to provide both strategic advantages and ease of understanding for artificial intelligence systems and humans who design them. In fact, the enterprise benefits of xAI extend far beyond these limited aspects, with great potential to improve security, build trust and improve decision-making in an enterprise.

The primary benefit of explainable AI is its potential to reduce the fear of machine learning capabilities and expand the range of applications for AI. xAI allows for designing and evaluating machines in more intuitive ways. Explainable AI may also encourage more companies and researchers to use AI in their products.

These systems are much easier to test and update. They can also be useful for training new AI systems on existing data.

How can you implement Explainable AI in your business?

Start with a basic xAI model – Introducing Explainable AI to the life of your customers starts with the building of simple but very informative xAI models. By this, we mean a model in which you don’t know all the intricate details of the algorithm, but all the crucial ones that allow you to perceive the AI model and interpret its output.

It is essential to know what to do when planning the future implementation of xAI into the life of your organization.

There are two main steps involved in implementing xAI. The first step is to develop a model that can be used to generate the desired outputs. The second step is to create a model that can explain why the outputs are what they are. The difference between these two models is how many variables are included. The first model is an explanatory model, including all of the variables that affect the output. The second model is an interpretive model, which only includes the necessary variables for the model to work.

Challenges of Explainable AI

One challenge that artificial intelligence researchers face in solving Explainable AI is a lack of understanding of the diversity of human perspectives.

Interaction with humans, or the lack thereof, is an important factor that researchers can consider when designing AI systems. For example, existing AI systems often feature “automated advice,” where humans answer the machine’s questions to further the learning process. Unfortunately, many of these systems have to be fed large amounts of data before they can answer questions correctly. This can cause a severe bottleneck in learning since algorithms are prone to time-consuming computations.

The cost of explainable AI

It’s different for every use case. It depends on the process, the data you use, and the training models. And it doesn’t directly come down to money.

It’s not even about engineers and data scientists. It’s about building a world-class process for explaining AI outputs to a broader audience.

It takes a lot of vision!


Explainable AI will continue to be developed and used in healthcare, environment protection, home automation, and many other industries. The study of explainable AI is set to benefit many areas of technology. We believe that explainable AI is key to building autonomous intelligent systems that are not only accurate but also can explain their actions. 

At HealthWorksAI, Explainable Artificial Intelligence is successfully being used to build, test, and optimize Medicare Advantage data to help healthcare payors boost their enrollment growth and achieve maximum efficiency of three main verticals – Product, Network and Marketing.

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