Understanding Explainable AI (XAI): Beginner’s Guide for 2025

Have you ever wondered if your favorite AI model is biased? 

According to the American Bar Association, every human being is a little bit biased, and the biases we live with can affect everything we do, from how we relate with others to how we process information. The latter part presents a unique challenge for AI developers trying to create unbiased, fair, and transparent AI models. 

Will they be able to do it, or will their inherent bias affect the AI models that they create?

More importantly, how do these developers create AI models that are reliable and trustworthy?

Enter Explainable AI!

Explainable AI not only helps ensure that a model is accurate and fair, but it also serves a range of other critical functions. As you read through this article, you’ll gain a deeper understanding of these capabilities.

We have tried to explain these concepts in easy-to-understand language so that you can have a working knowledge of explainable AI after reading this article. 

Without further ado: 

What is Explainable AI (XAI), and Why is it important?

Traditional AI systems are like black boxes. Most people can’t really know how they do the amazing things they do. This makes it difficult for humans to trust them. 

Explainable AI models, on the other hand, are designed with transparency in mind. They typically offer insights into how decisions are made. It’s like being able to accurately describe how OpenAI’s chatGPT analyzes its enormous library of datasets before producing that nice, well-written answer to your queries. 

Therefore, the transparency integrated into explainable AI models helps to:

1. Build trust: Regular people (especially those who aren’t tech savvy) are more likely to adopt AI technology if they understand how AI models make decisions. 
   
   
2. Ensure fairness: Explainable AI helps identify biases in decision-making processes and thus contributes to more equitable outcomes. For instance, job applicants can trust an AI-powered applicant ranking system if they know the exact processes it uses to rank them when applying for a job. 
   
   
3. Enhance accountability: Explainable AI makes it possible for developers and users to trace errors and improve the reliability of AI decision-making. Like in the instance of the job applicants, the developers and applicants involved with that system would be able to trace what went wrong if the AI-powered tracking system ranked them poorly or highly for that matter. 

To make this easier to grasp, consider an AI-powered medical diagnosis system. If a patient is flagged as having high risk by the AI system, XAI can provide a clear explanation of the factors that have contributed to these diagnoses, such as age, medical history, or specific test results. 

If this transparency can be established, everyone will have a lot more trust in the system. For starters, doctors would trust and rely on it more because they’re now aware of a standardized process to test its patient recommendations. This transparency builds trust and helps doctors validate and act on AI-driven insights.

History of Explainable AI

Given their potential impact on humans, explainable AI systems are more important than ever. Since at least the 1970s, AI explainability has been a crucial component in developing an AI system. To explain common diagnostic reasons, like treating blood infections, the symbolic reasoning system MYCIN was created in 1972.

Later on, truth maintenance systems (TMSes) were created in the 1980s and 1990s to expand AI’s capacity for reasoning. TMSes, for instance, were employed in inference systems that relied on logic and rules. By following an AI’s logic through rule operations and logical deductions, a TMS can monitor its reasoning and conclusions. Every AI thinking is explained by this method. 

What is the Significance of Explainability of Trusted AI Systems?

AI is a burgeoning technology with fast adoption rates across industries. However, skepticism and even distrust continue to be barriers to adopting the technology. So many people are aware of its capabilities, yet they’re unsure of the dangers it poses. For example, many people do not trust AI because they have no power over its development process, while others cite privacy and data protection concerns.

Therefore, there is a need to create a way in which people can trust these AI systems if the adoption of AI across industries is to increase and even be maintained at the very least. Individuals and organizations do not want to trust them blindly. 

With XAI, users and stakeholders can access interpretable results in AI decision-making. Furthermore, they can also get a clearer and better understanding of the processes through which AI gets its results. Whether it is machine learning (ML), deep learning (DL), or neural networks, XAI makes it easier to validate outcomes and address concerns such as biases and errors. 

For example, in self-driving vehicles, passengers need to know why a vehicle made a certain decision, such as stopping abruptly, or they might panic and distrust the car’s AI system. XAI provides clarity by explaining why the vehicle took the decision. 

XAI and its Contribution to Fairness, Accountability, and Transparency 

We started out by saying that humans are all biased. And that if not carefully handled, these biases can be transferred to AI models. For example, an AI model can disproportionately deny loans to certain demographics because of biases in training data.

XAI reduces these biases by highlighting the underlying causes for bias, thus allowing developers to address them. Similarly, AI developers can be held more accountable when decisions can be audited and explained. Furthermore, XAI ensures that developers comply with ethical and legal standards.

Explainable AI vs Traditional Black Box systems

Traditional AI models, which are typically called black boxes, are complex and hard to interpret. In contrast, XAI (white box) is designed to aid easy interpretation, and as such, their decision-making process is transparent and understandable.

Additionally, black box systems typically prioritize accuracy and efficiency, while XAI focuses on balancing performance with transparency. For example, while black box models might excel at predicting outcomes, XAI models help you understand the reasoning behind the predictions–they explain and justify them. 

Let us consider the use of an AI model in sorting and ranking loan applicants in a financial situation. The main goal of the AI system might be to analyze vast amounts of data about different loan applicants and score them based on their likelihood of paying back the loan. In this case, the AI model will be analyzing data points like credit score, transaction volume, and many other financial information.

With black box models, a loan application can be denied after analyzing these criteria, leaving the customer distraught. But that’s where it ends; a black box AI model won’t provide the series of steps or reasons for denying such a loan application. 

On the other hand, XAI models help the loan applicant understand why their loan application was denied by providing additional information and the transparent process through which it analyzed their financial details. As we have said earlier, this could be due to a low credit score or insufficient income. This allows them to address these issues and improve their chances of a successful loan application in the future. 

Comparisons and Related Concepts

Below are some concepts that are often associated with explainable AI but are different from it. It is important to understand the core differences between these concepts so that you can get a better idea of XAI. 

Explainable AI vs Interpretable AI

While both concepts price transparency, the difference between these concepts is in their approach to achieving it. Explainable AI often involves explaining complex models after producing results based on live data, while interpretable AI focuses on designing simple models. 

In this context, interpretability talks about how easy it is for an observer to understand the reason behind an AI system’s decision, like in the case of the loan application. On the other hand, explainability further breaks down the entire decision-making process for the observer. This breakdown process makes it easy for the observer to trace the system’s process if they suspect an error. And this is why XAI is more trustworthy compared to AI systems. 

Explainable AI vs Generative AI

XAI ensures transparency in decision-making, while whole generative AI focuses on creating content. A combination of the two can lead to more accountable generative AI systems, such as those used in creative industries or automated journalism. 

Application of XAI Across Industries 

Because XAI improves transparency, reduces biases, and encourages accountability, it has been adopted rapidly for various operations across industries. Some of them include the following:

1. Healthcare

In the healthcare industry, XAI improves diagnostic tools by providing insights into AI predictions. For example, in a cancer diagnosis, XAI can highlight certain features in medical images that led to a diagnosis. This helps radiologists validate AI findings and gain confidence in the system.

2. Financial Services

XAI improves the transparency of financial services like credit scoring and fraud detection. Concerning the latter, XAI helps explain why a transaction is flagged as suspicious, thus increasing trust among customers and regulators. It also helps financial institutions comply with stringent regulatory requirements. 

3. Military Applications

In the military, XAI guarantees that AI systems used in making important decisions, such as target identification, are reliable and transparent. This helps military operatives to make confident choices in life-or-death scenarios. It also ensures that human oversight remains necessary for decision-making. 

For example, autonomous drones that use XAI can explain their targeting decisions, such as identifying threats based on specific patterns or behaviors. This ensured that the commanders could trust the system while being in charge.

4. Autonomous Vehicles

Many people believe autonomous vehicles are the future. They have the capacity to make roads safer, reducing accident rates. However, many people don’t trust them enough right now. They’re often concerned about the fact that they have no control if the car suddenly malfunctions. 

To ensure rapid adoption, manufacturers need to implement XAI systems that help passengers understand how their autonomous vehicles make decisions, such as the specific route they choose and reasons for emergency stops. 

One notable application of XAI in the automobile industry is Tesla’s autopilot system, which uses XAI to provide feedback on decision-making processes such as lane selection or braking patterns.

Most of these applications of XAI were distant fantasies a couple of decades ago. However, they are fast becoming the reality today, and one can’t help but wonder how they came to be. If you have similar thoughts, you need to understand some of the prominent techniques and tools that help to pull this off. 

Let’s examine them in the next section. 

Techniques and Tools in XAI

Here are some techniques and tools used in developing an Explainable AI model.

1. Techniques for Explainable AI machine learning

Some popular techniques used in training an XAI model include decision trees, rule-based systems, and attention mechanisms that focus on interpretability and performance. For example, decision trees offer a clear and logical representation of decision paths. This makes them perfect for scenarios that require high transparency. 

2. Understanding SHAP and LIME

Many XAI developers use tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret predictions by AI models. These tools help users understand which features used in prediction are more important and what decision pathways are (how an AI model arrives at a decision). As such, developers and stakeholders can rely on these models’ output.  

For example, SHAP values can break down predictions into contributions from each feature and offer a detailed view of the decision-making process.

3. Visualization Tools for Explaining AI Outputs

Heat maps, decision plots, and other similar visualization tools help users understand AI decisions intuitively, making complex output easier to understand. For example, heatmaps in medical imaging help doctors to focus on specific areas of concern.

Limitations of XAI

While XAI can significantly improve the application of AI models in real-life scenarios, it comes with its own limitations. These include:

1. Performance trade-offs

In complex AI models like deep learning networks, enhancing interpretability can sometimes make these models less accurate. 

2. Privacy concerns

In the bid to be transparent, companies can inadvertently expose sensitive information about their operation, leading to ethical and legal questions. 

The Role of Data and Algorithms In XAI

The data set used in training an AI model determines its reliability. Therefore, when developing an XAI model, developers prioritize training data sets that represent diverse populations to ensure that outcomes are fair and accurate. For instance, AI models deployed in financial services that are trained on biased data may perpetuate existing inequities, undermining trust and effectiveness. 

Additionally, when designing AI models, developers must prioritize explainability in designs of AI algorithms. This translates into outputs that are easy to understand even without compromising performance. Techniques such as rule-based systems and interpretable neural networks are examples of this approach.

Conclusion

AI evolution and adoption have reached the point where it isn’t just about jumping straight into using the next shiny piece of AI technology. Individuals and organizations have become increasingly aware of the potential risks associated with AI use. As a result, the AI industry, in search of trust, is trying to embed frameworks and systems like explainability into the AI development process. 

Compared to the traditional AI development process, prioritizing explainability helps develop fairer, more transparent, and less biased AI frameworks and tools. The average user can understand why and how an AI model makes a decision, and they can also trace these processes anytime they sense an error. 

These kinds of AI systems are more trustworthy and safer, and that’s what users want right now. If you would like to integrate explainability into your AI development process, you can explore Debut Infotech’s AI development services. They’re adept at creating and seamlessly integrating AI systems that are fair, trustworthy, and less biased. 

Frequently Asked Questions (FAQs)