What is Explainable Artificial Intelligence (XAI)

Consider a steel tubing production line where workers operate heavy, possibly dangerous equipment. Company leaders employ a team of machine learning (ML) experts to create an artificial intelligence (AI) model that can assist frontline workers in making safe decisions, with the hopes of revolutionizing their business by increasing worker efficiency and safety. Manufacturers release their sophisticated, high-accuracy models to the manufacturing line after an expensive development phase, hoping to see their investment pay off. Instead, they see only a small percentage of their employees adopting the technology. What went wrong, exactly?

This hypothetical example, based on a real-life case study in McKinsey’s “The State of AI in 2020,” highlights the importance of explainability in the AI world.

While the model in the example was safe and accurate, the target users did not trust the AI system because they did not understand how it made decisions. End-users have a right to understand the decision-making processes underlying the systems they are expected to use, especially in high-stakes situations. Unsurprisingly, McKinsey discovered that improving system explainability led to increased technology adoption.

That’s where Explainable Artificial Intelligence comes in, which we’ll explore in this article. We’ll start with the definition and basics of XAI, move on to the gaining traction and current limitations of XAI, and finish with the real-life applications of XAI.

Table of Contents

What is explainable artificial intelligence?

The Basics of Explainable AI

Why is XAI gaining so much traction?

Explainable AI’s Current Limitations

Real-life applications of XAI


What is explainable artificial intelligence?

Explainable artificial intelligence (XAI) is a set of processes and strategies that allow human users to understand and trust machine learning algorithms’ results and output. The term “explainable AI” refers to a model’s likely biases and expected impact. In AI-assisted decision-making, it aids in the evaluation of model correctness, fairness, transparency, and outcomes. The ability of an organization to explain AI models is crucial when it comes to bringing AI models into production. AI explainability also supports in the adoption of a responsible AI development strategy by an organization.

Humans will find it more difficult to comprehend and retrace how AI arrived at a conclusion as AI improves.The entire calculating process is transformed into a “black box” that is impossible to decipher. The data is used to generate these black box models. Even the algorithm’s creators, the engineers and data scientists, have no idea what’s going on inside them or how the AI algorithm arrived at a particular outcome.

The Basics of XAI

Explanations can take many different forms depending on the situation and goal. Human-language and heat-map explanations of model activities are shown in Figure 1. The ML model presented below is designed for doctors to utilize and can diagnose hip fractures using frontal pelvic x-rays. Based on the x-ray on the far left, the original report gives a “ground-truth” report from a doctor. The resulting report includes an explanation of the model’s diagnosis as well as a heat-map of the x-ray locations that influenced the conclusion. The ‘Generated Report’ offers doctors an easy-to-understand and validated explanation of the model’s diagnosis.

radiologist-quality reports

Figure 1: From “Producing radiologist-quality reports for interpretable artificial intelligence,” a human-language explanation

Figure 2 shows a very technical, interactive depiction of a neural network’s layers. This open-source software allows users to play around with a neural network’s architecture and see how the individual neurons evolve over time. Heat-map explanations of underlying ML model architectures can provide crucial information about the inner workings of opaque models to ML practitioners.

heat maps of neural network layers

Figure 2: TensorFlow Playground heat maps of neural network layers.

Explain ability attempts to provide answers to stakeholders’ inquiries concerning AI decision-making processes. ML practitioners and developers can utilize these explanations to guarantee that project requirements for ML models and AI systems are met during development, debugging, and testing. Non-technical audiences, such as end-users, can benefit from explanations to obtain a better knowledge of how AI systems work and to address issues and concerns regarding their behavior. This greater transparency aids in the development of trust as well as the monitoring and auditability of the system.

Explainable AI techniques have been created and utilized at various stages of the machine learning lifecycle. There are techniques for assessing the data needed to generate models (pre-modeling), including including interpretability into a system’s architecture (explainable modelling), and creating post-hoc explanations of system behavior (post-hoc explanations) (post-modeling).

Why is XAI gaining so much traction?

IBM XAI delves into the inner workings of obfuscated models. Unlike many preceding models, these models are more difficult to comprehend and manage due to their architecture. Given the current climate of rising ethical issues around AI, transparency is especially critical. Individuals who are impacted by decisions made through “automated processing,” according to the European Union’s General Data Protection Regulation (GDPR), are entitled to “meaningful information about the reasoning involved.”

Explainable AI’s Current Limitations

One stumbling block for XAI research is a lack of agreement on the definitions of some essential concepts. Explainable AI is defined differently in different studies and circumstances. Explainability and interpretability are terms used interchangeably by certain researchers to refer to the concept of making models and their outputs intelligible. Others distinguish the phrases in a variety of ways. According to one academic source, explainability relates to a priori explanations, whereas interpretability refers to explanations after the fact. To establish a common vocabulary for discussing and investigating XAI topics, definitions within the domain of XAI must be improved and clarified.

While there are many articles offering new XAI techniques, there is a scarcity of practical advice on how to choose, apply, and test them. Explanations have been demonstrated to help non-AI specialists comprehend ML systems, but their capacity to establish trust among non-AI experts has been questioned.

Now let’s take a look at some high-stakes industries where XAI can help.

Real Life Applications of Explainable Artificial Intelligence


The potential benefits of AI in healthcare are significant, but the risk of an untrustworthy AI system is even greater. It goes without saying that AI models’ recommendations to help clinicians categorize crucial diseases using structured parameters or unstructured data such as medical imaging have far-reaching implications. If an AI system predicts and also explains why it came to that conclusion, it will be far more beneficial than if it predicts and then allows clinicians to spend an equal amount of time (with or without AI judgments) determining whether the AI system’s decision is accurate and trustworthy. Because lives are on the line in healthcare, XAI is critical.


BFSI has a lot to gain, and XAI has the potential to transform the business. In banking and insurance, artificial intelligence (AI) has been widely used to analyze credit risk. Higher financial stakes exist when denying a loan, inflating or deflating premium costs for health or motor insurance, or making incorrect stock trading recommendations.


Autonomous driving has been a developing subject and is the industry’s future. Self-driving autos or driverless cars are exciting as long as no mistakes are made. In this high-stakes AI application, one erroneous action can cost one or more lives. Explainability is essential for understanding the system’s capabilities and limits prior to deployment. Understanding the weaknesses of driving assistance (or auto-pilot) in the field when utilized by customers is critical in order to assess, explain, and rectify the issues as quickly as possible. To a significant extent, assistive parking and voice assistants are appealing features that involve the model making relatively low-risk decisions.

The Legal System

In Western countries, AI systems are increasingly being used in decision-making in the judicial process. Pro Republica has exhaustively documented the inherent bias that comes with it towards a single ethnic community in the past. Bias in AI applications, such as giving parole based on the likelihood of a repeat offense, has far-reaching repercussions, and fairness in them is essential because they involve an individual’s rights and liberties.

To summarize, XAI is an area of research and development that is rapidly gaining traction.


Read more:  Federated learning


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