In a world where you can transform everything using artificial intelligence (AI) capabilities, think about a helping hand that helps you make the right decisions. Likewise, in the realm of computers and AI, smart programs make intelligent decisions; for example, they help recommend products. The concept of Explainable AI (XAI) comes into the picture now.

XAI focuses on the reasoning aspect behind the decisions made. However, we should understand that not all AI comes under explainable AI. Let’s discuss explainable AI in detail.

What is Explainable AI?

Explainable AI involves a series of methods that facilitate human users to understand how and why the AI has arrived at a particular decision or outcome. XAI assumes great significance as it has a key role in fairness, accountability, and transparency in the machine learning model. Explainable AI is ideal for building trust when you use AI. More importantly, XAI helps you understand and interpret the behavior of an AI model.

For example, there are issues like AI bias, where there is an anomaly in the results of the ML algorithm due to the prejudiced assumptions in the training data.

The explainable AI tools help users identify and reduce the issues of interpretability. The efficiency in interpretation enhances the trust in the ML model, particularly in credit business, law, and healthcare services.

How Does Explainable AI Function?

Explainable AI uses several methods to explain the decision-making process behind the AI models. We shall discuss a few of them below:

Explanation Graphs

Depict how an AI model processes the information/data. For example, the explanation graph shows the user’s purchase history if you use an AI model to recommend products and services.

Decision Trees

Typically a flowchart-like structure, decision trees show you how the AI model has made a particular decision and the factors that influenced it while arriving at such a prediction.

Local Explanations

As the name suggests, they explain why an AI model makes specific decisions. For example, if you use AI models for recommending products, local explanations show the products under consideration and the reasons for selecting them.

Read more: Generative AI | Top Use Cases and Benefits in 2024    

Examples of Explainable AI

XAI is used in several industries, such as finance, healthcare, self-driving vehicles, and many more. Let’s briefly explain how XAI helps several industries in decision-making.

Financial

XAI helps detect financial discrepancies and sanctions or denies claims on loans, mortgages, and so on. On top of it all, they help predict market price fluctuations.

Healthcare

XAI helps diagnose patients and creates an environment of trust between physicians and the system. Further, it helps physicians understand how an AI model arrives at a diagnosis.

Self-driving Vehicles

XAI helps explain autonomous driving decisions, particularly safety-related ones. Passengers can understand the reasons behind the decision-making while they travel in a self-driving car, feel much safer, and help understand the situations that can be handled.

What are the Benefits of Using Explainable AI?

XAI imparts interpretability and transparency to AI models. Its benefits include:

• Verifies if the AI system is unbiased by helping you understand why an AI system has arrived at a particular decision
• Enhances trust in AI models among enterprises and consumers as it explains reasons for decisions
• Identifies malicious attacks on AI models and prevents instances of incorrect decisions
• Developers can find issues and fix them due to increased transparency and interpretability
• Assists you in making informed decisions and offers analytical insights to enhance productivity
• Encourages better decision-making by using predictive models to influence the predicted results
• Enables you in faster AI optimization through monitoring and analyzing AI models you use to ensure its accuracy
• Increases the adoption of AI models in your enterprise as it imparts reliability and trustworthiness into the system.

Read more: Anomaly Detection Using Artificial Intelligence and Machine Learning for Quality Assurance

Data and Explainable AI

To improve the explainability of an XAI model, you need to pay heed to the data used for training. Therefore, at the design stage, the development team must decide the data details used for training an algorithm. They also need to check the authenticity of data, whether it is prejudiced, and, if so, what is needed to reduce the bias. Also, you need to remove irrelevant data to arrive at the most accurate predictions.

How Do You Adopt XAI in Your Business?

If you want to make the most of XAI for your business, consider focusing on the following key aspects:

Define AI Ethics

Ensure your AI adoption is safe and honest and inspires other enterprises. You should set up your principles, and it’s the first step in your AI adoption.

Create Your AI Strategy

Keep in mind that your AI strategy should match your business goals and the ways by which AI can help achieve them. Moreover, it should cover the ways to reduce expenses, improve productivity, methods to build AI capability, and the persons who can access the AI tools.

List out Your AI Applications

After you have specified how AI can assist your enterprise, identify its business applications. You can use it to automate business processes, make predictions, and generate marketing content. 

Create Skills and Capabilities

Finally, you have to make all efforts to enhance the AI knowledge among your teams and the AI capabilities to explore its utility in the long run in your enterprise. You can help upskill your employees or hire experts with the needed skills, whichever is ideal.

Frequently Asked Questions

When was explainable AI first introduced?

The Defense Advanced Research Projects Agency introduced XAI in 2017. It focused on developing AI systems that facilitate explanations for decisions made. 

What exactly is explainable AI?

Explainable AI is a set of methods that helps users understand and trust the outcomes created by machine learning algorithms.

What is the objective of XAI?

The purpose of XAI is to explain the reasons behind the decision processes made by machine learning algorithms. It helps identify biased outcomes arising from a lack of quality in training data.

Final Words

The adoption of AI and its usability may vary between enterprises and industries. However, laying the foundation is a big step toward adopting AI systems within your enterprise. In the first stage, you must decide on the ethics, objectives, use cases, internal skills, and capabilities. In any case, the adoption of technology requires adherence to best practices.

At ThinkPalm, we offer the best AI development services to drive your technology transformation to the next level. Explore our AI development services and AI solutions that focus on revolutionizing your business. Our AI expertise helps you automate your tasks and resolve complex business challenges. Reach out to us today to take the first step toward automating your business process and ensuring competency in all areas.

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