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Explainable AI (XAI): Working and Applications

Bhumika Dutta
Mar 29, 2023

Over time, Artificial Intelligence has proved to have enormous promise. Many new things are emerging in AI and ML, one of which is the explainability of AI. The strategies used by a model to interpret its findings in a way that people can comprehend are referred to as AI explainability.

Explainability should not be mistaken with the interpretability of artificial intelligence in this context, since the two concepts differ in a subtle but significant way.

In terms of technical distinction, interpretable models are rational processes interpretable by humans that can recreate model findings. But, an explainable model will first construct a black box model and then provide a post-hoc explanation for how the model came up in the condition it is in.

Interpretability aids people in understanding the cause and effect of a model prediction, whereas XAI aids in dissecting the internal mechanics of a black box to comprehend the significance of various aspects and the judgments they might lead to.

In this article, let us learn how explainable AI works and where it is used in the real world.

The Concept of Explainable AI

XAI, or Explainable AI, is a set of techniques and tactics that enable human users to comprehend and trust the findings and output of machine learning algorithms. In other words, it is a component of AI capability that is responsible for describing how the AI arrived at a certain outcome.

To understand the logic behind any decision made by AI, we need to compare the data input, patterns recognized by the model, and the model prediction output. These elements are essentially the basis of AI explainability deployed in decision-making systems.

In this scenario, the explanation might be characterized as how precisely the system gives insights to the user. This can be given as information on how the model arrived at its conclusion, a decision tree route, or data visualization, depending on the implementation approach.

This helps individuals trust and understand what is going on, rather than feeling as though their information is being exploited or used without their permission. This is significant because many people are concerned about the expanding use of artificial intelligence in our lives. If we are to rely on these systems, we must be able to trust them.

Also Read | What is Explainable AI?

The Working of Explainable AI

The motive of explainable AI is to improve the transparency, trustworthiness, fairness, and accountability of AI systems. Explainable AI Systems can aid in understanding the logic behind a specific prediction or conclusion produced by machine learning algorithms.

This type of AI has grown in importance as a growing number of stakeholders have begun to challenge AI's forecasts. They want to know how the forecasts were formed before they depend on them and make decisions based on them. The illustration below depicts the necessity for explainable AI.

The Queries of a user regarding AI prediction models (Source)

Explainable AI works on four main principles:

Explanation
Meaning
Accuracy
Knowledge limitations

Explanation:

All system outputs are accompanied by evidence or reason(s). The Explanation principle demands AI systems to offer proof, support, or justification for each output. This idea does not imply that the evidence is correct, instructional, or explanatory in and of itself; rather, it states that a system can give an explanation.

Meaning:

Individual users can comprehend system explanations. The system fulfills the Meaningful principle if the recipient understands the system's explanations. This idea is frequently satisfied if the user comprehends the explanation and/or it is useful in performing a task. This principle does not imply a one-size-fits-all solution. Different explanations for different categories of users may be necessary for a system. Explanations may be tailored to each user group thanks to the Meaningful concept.

Accuracy:

The description correctly depicts the output-generating process of the system. When used together, the Explanation and Meaningful principles simply need a method to create explanations that are meaningful to a user group. These two concepts do not need the plan to offer an explanation that precisely describes the output creation process of the system.

The Explanation Accuracy principle requires that the explanations provided by a system be correct. Explanation correctness is different from decision accuracy. Decision correctness in decision tasks refers to whether or not the system's judgment is correct.

Whatever the system's judgment is, the accompanying explanation may or may not accurately describe how it arrived at that outcome. AI researchers have developed standard measures for algorithm and system accuracy. While there are established decision accuracy metrics, scholars are actively working on performance metrics for explanation correctness.

Knowledge Limitations:

The system only operates in the conditions under which it was designed, or when the output of the system is sufficiently trustworthy. The previous notions suggest that a system functions within the limitations of its knowledge. According to this Knowledge Limits notion, systems recognize conditions in which they were not designed or approved to function, or in which their replies are not reliable.

This method safeguards replies by identifying and communicating knowledge constraints, ensuring that no judgment is made when it is not required. The Knowledge Limits Principle can boost system confidence by removing misleading, damaging, or unjust decisions or outputs. A system's knowledge restrictions might be approached in one of two ways. First, the investigation may fall outside the purview of the system.

The Difference between AI and Explainable AI

The primary distinction between AI and explainable AI is that explainable AI is a type of AI that has explanations for its judgments. Explainable AI approaches are highly impacted by how people make inferences and draw conclusions, allowing them to be duplicated within an explainable artificial intelligence system.

Explainable AI may assist in explaining the logic behind a machine learning model's choice learned using any technique such as linear regression, random forest, decision trees, and so on. This is one of the most prevalent explainability strategies used in practice, and there are several tools available that support it.

Explainable AI systems may be constructed using a variety of strategies that focus on reasoning via the explanation of data or prediction through the explanation of decisions made by an artificial intelligence algorithm.

Among the most prevalent explainability strategies are:

Decomposition (cutting down a model into distinct components) (breaking down a model into individual components)

Visualization (adding explainability characteristics to explain model predictions in a visual fashion)

Mining for Explanations (using machine learning techniques for finding relevant data that explains the prediction of an artificial intelligence algorithm).

Applications of Explainable AI

Where robots create or play a prominent part in lending choices and other financial services, explainable AI is a strong fit. Many of these applications are transferable to different sectors; the specifics may differ, but the concepts remain the same, thus these examples may help you think of explainable AI use cases in your firm.

Applications of Explainable AI

Healthcare:

The potential benefits of AI in healthcare are significant, but the risks associated with an untrustworthy AI system are much greater. It goes without saying that AI models' judgments to aid clinicians in categorizing important conditions, whether utilizing structured criteria or unstructured data such as medical imaging, have far-reaching repercussions.

If an AI system predicts and explains the reasons for its conclusion, it will be far more helpful than a system that predicts and allows clinicians to spend an equivalent amount of time (with or without AI judgments) determining if the AI system's decision is correct and trustworthy. Because lives are at risk in healthcare, XAI is crucial.

Automobiles:

Autonomous driving is a developing subject and the automotive industry's future. Driverless or self-driving automobiles are exciting as long as no mistakes are committed. 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 implementation.

Understanding the flaws of driving assistance (or auto-pilot) on the road when utilized by customers is critical so that the causes of deficiencies may be analyzed, clarified, and prioritized. Assistive parking and voice assistants are both appealing features that consist of relatively low-risk judgments performed by the model. However, in other circumstances, such as brake assistance or self-driving, XAI becomes critical in recognizing and correcting bias.

BFSI:

BFSI has a lot to gain, and XAI has the potential to change the business. AI-based credit risk assessment has become widely used in banking and insurance. Premium estimation based on multimodal data is gaining traction in industrialized nations, with pay-as-you-drive and pay-how-you-drive models that employ machine learning for decision-making.

This has a substantial impact since most AI systems used in risk assessment, premium estimates, and other choices are black-boxed models. XAI systems that are capable of producing excellent outcomes while also delivering understandable explanations would generate adequate confidence and meet regulatory standards, leading to further use of AI solutions in the sector.

Denying a loan, inflating/deflating premium costs for health/motor insurance, or making incorrect stock trading recommendations all have large financial consequences; hence, XAI may be envisioned as a beneficial addition in explaining such judgments to the impacted party.

Judicial System:

In Western nations, there is a growing use of AI systems in judicial decision-making. ProRepublica has thoroughly studied the inherent bias that might come with it toward a single ethnic community in the past. Bias in AI applications, such as giving parole based on the likelihood of repeat offending, has far-reaching repercussions, and fairness in them is required since it deals with an individual's rights and liberties.

Insurance:

With such a big impact on the insurance industry, insurers must trust, understand, and audit their AI systems in order to fully utilize their capabilities. Explainable AI has proven to be a game-changer for several insurers. As a result of employing it, insurers report greater customer acquisition and quotation conversion, increased productivity and efficiency, and decreased claims rates and fraudulent claims.

Manufacturers:

Explainable AI might be used to explain why a manufacturing line is malfunctioning and how it needs to be adjusted over time. This is critical for increased machine-to-machine communication and comprehension, which will aid in the creation of higher situational awareness between people and robots.

Human Resources:

Explainable artificial intelligence might be used to determine whether or not a resume was chosen. This increases understanding between humans and computers, which contributes to higher confidence in AI systems while decreasing prejudice and unfairness concerns.

Defense:

Explainable AI may be used in military training applications to explain the reasons behind an artificial intelligence system's choice (i.e., autonomous vehicles). This is significant because it helps to reduce any ethical issues, such as why it misidentified an item or failed to fire on a target.

Challenges of Explainable AI

Here are some problems with explainable AI:

Explainable AI is a very young field of study, and there are several ongoing difficulties that explainable models present today. One difficulty is that explainability might come at the price of model performance accuracy because explainable artificial intelligence systems perform worse than non-explainable or black-box models.

One of the most difficult problems in Explainable AI is generating explanations that are both accurate and intelligible.

Another significant difficulty with explainable AI is that explainable AI models might be more difficult to train and tune than non-explainable machine learning models.

Another issue is that explainable AI systems might be more difficult to implement since explainability features often require some amount of human engagement.

Explainable AI for the Future

AI has drawbacks such as prejudice and injustice. These will cause trust concerns with AI in the future. Explainability and explainable AI are two approaches to addressing these issues. Explainability approaches are gaining traction in this area, as they are anticipated to lead to improved human-machine interaction, more responsible technology (such as autonomous cars), and enhanced trust between machines and humans.

Explaining artificial intelligence system decisions can assist offer insight into how the model makes its judgment. Explainable AI, for example, may be used to explain why an autonomous car did not stop or slow down before colliding with a pedestrian crossing the street.

Explainable AI is an important aspect of the future of artificial intelligence since explainable AI models explain the reasons behind their actions. This increases understanding between humans and machines, which can aid in the development of trust in AI systems. To summarise, XAI is unquestionably a growing area of study and development.

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Explainable AI (XAI): Working and Applications

The Concept of Explainable AI

The Working of Explainable AI

Explanation:

Meaning:

Accuracy:

Knowledge Limitations:

The Difference between AI and Explainable AI

Applications of Explainable AI

Healthcare:

Automobiles:

BFSI:

Judicial System:

Insurance:

Manufacturers:

Human Resources:

Defense:

Challenges of Explainable AI

Explainable AI for the Future

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