An introduction to the four principles of explainable AI

Do you trust your artificial intelligence systems?

As computer science and AI development have continued to advance, complexity and decision-making processes have become more difficult to comprehend. This has raised concerns about the transparency, ethics, and accountability of AI systems.  

If you feel like your AI tools are a work colleague you’ll never understand (like Greg from IT), the field of explainable artificial intelligence (XAI) is here to help. With a market forecast of $21 billion by 2030, explainable AI technology will be pivotal to bringing transparency to the machinations of computer minds. 

Explainable AI…explained 

Explainable AI refers to the development and implementation of AI systems that provide clear types of explanation for their decision-making processes and machine-learning-algorithm output. The goal is complete insight into how the robot thought through its problem solving. IBM sums this up as “a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability.”

Right now, black-box models are a major issue in the AI-application world. When the decision-making process of an AI system is not easily understandable by humans, that’s an alarming situation, to say the least. From our human perspective, it should be relatively easy to know why a decision was made during AI work.

A lack of transparency can lead to issues with trust, as end users may be understandably hesitant to rely on a system when they don’t understand how it works. Plus, ethical and legal issues can arise when an AI-based system is making biased or unfair decisions.

Explainable AI systems aim to solve the black-box problem by providing insights into the inner workings of AI models. This can be achieved through various methods, such as visualizations of the decision-making process, or through techniques that simplify the model’s computations without sacrificing accuracy. 

The four explainable AI principles

Data-science experts at the National Institute of Standards and Technology (NIST) have identified four principles of explainable artificial intelligence.

At its core, they say, explainable AI is governed by these concepts: 


The primary principle is that AI systems should be able to provide clear explanations for their actions. No “Umm…” or “Well, it’s kind of like…” allowed. This involves elaborating on how they process data, make decisions, and arrive at specific outcomes. 

For example, a machine learning model used for credit scoring should be able to explain why it rejected or approved a certain application. In this scenario, it needs to highlight how vital factors like credit history or income level were to its conclusion. 


The explanations provided by AI systems need to be understandable and meaningful to humans, especially non-experts. Convoluted, technical jargon won’t help a user understand why a certain decision was made. It will just lead to more confusion and lack of trust. 

For example, the healthcare sector is famous for its technobabble (just watch Grey’s Anatomy). If an AI system is used for diagnosing diseases, it should present its findings in a way that both doctors and patients can understand, focusing on key factors that led to its diagnosis (such as high blood pressure or being obese). Otherwise, doctors can’t confidently prescribe appropriate treatment, and the consequences could be severe. 

Explanation accuracy 

Yes, AI systems must provide explanations. And it’s equally important for these explanations to be accurate. But how can Roberta the Robot ensure that her explanations are, indeed, accurate? 

This involves using methods such as feature importance ranking, which highlights the most influential variables in a decision-making process. Other techniques include local interpretable model-agnostic explanations (LIME) and SHapley additive exPlanations (SHAP), which provide local and global explanations of the model’s behavior. 

Knowledge limits 

We all have limits that we’re generally aware of, and AI should be no different. It’s crucial for AI systems to be aware of their limitations and uncertainties. A system should operate only “under conditions for which it was designed and when it reaches sufficient confidence in its output,” says NIST.

Imagine that an AI system is predicting stock market trends, for instance. The degree of uncertainty or confidence in the model predictions should be able to be articulated. This could involve displaying error estimates or confidence intervals, providing a more complete picture that could lead to more-informed decisions based on the AI outputs.

The importance of explainable AI 

In the ever-changing world of technology, explainable AI is becoming more important due to these factors: 

Growing complexity with adoption of AI systems 

When people say robots are taking over the world, they’re not wrong. More sectors are adopting AI every day (think generative AI like ChatGPT and DALL-E), and the logistics are becoming more intricate as engineers continue to explore the capabilities.

If a decision tree becomes overly complex, it’s tougher to interpret the results. AI also uses multiple approaches, including convolutional neural networks, recurrent neural networks, transfer learning, and deep learning; this can muddy getting to the root of an AI explainability problem.

With all of that in mind, it’s crucial for stakeholders to understand AI decision-making processes. 

Autonomous vehicles are a notable example. They use AI systems to make critical decisions in real time. Without explainable AI in the mix, it would be difficult for engineers and developers to understand how these cars make decisions such as when to brake or swerve.

Ethical concerns and biases 

AI systems learn from input data they’re trained on. If this data contains bias, their decision processes are likely to be influenced by the bias, too. To say this realization has huge ramifications is an understatement. Explainable AI can provide much-needed transparency into the decision-making process, helping identify and correct bias.  

For example, because of a biased training data set, an AI system used to help an organization hire top talent might inadvertently favor certain demographics. With explainable AI, this bias could be identified and corrected to ensure that fair hiring practices are maintained. 

Grappling with these types of concerns, organizations such as LinkedIn are striving to create explainable AI-driven recommendation systems.

Legal and regulatory requirements

Governments and regulatory bodies are implementing laws to ensure AI’s ethical and responsible use. Who can blame them? Explainable AI can help organizations adhere to these regulations by providing transparency and clear evidence of how their AI tools stay in line. 

Take the European Union’s General Data Protection Regulation (GDPR). It requires organizations to explain their AI-made decisions. For example, in the financial sector, if AI were used to flag suspicious transactions, the organization would need to detail the unusual patterns or behavior that led the AI to highlight the transactions. Explainable AI would allow the organization to show hard data to regulators and auditors. This could help build trust and understanding between AI systems, their users, and regulatory bodies. 

Trust and confidence 

Would you trust a robot to look after your wallet? OK, it’s not quite like that (but not far off, either). With use of AI, trust of machines will always be an issue. If you don’t know how it’s joining the dots — the feature attribution — you don’t know exactly how the AI algorithms are working, how can you trust the results?   

In the retail world, AI-powered systems can help managers improve supply-chain efficiency by forecasting product demand to aid decisions about inventory management, for example. Highlighting key metrics, such as the average footfall in seasonal periods and popular trends, makes for confident decisions that can substantively lead to improved sales and customer satisfaction.

Decision-making support 

AI systems’ providing users with insights that help them make more-informed decisions is especially important in sectors like healthcare, financial services, and public policy. The decisions in various use cases in these sectors can have significant real-world impacts on individuals and communities. 

In healthcare, for example, explainable AI algorithms are used to analyze patient data and identify patterns that can help predict the onset of diseases like diabetes, heart disease, and different types of cancer. With explanations for the predictions, healthcare providers can better understand risk factors and make informed suggestions about preventive measures.

Risk mitigation 

Explainable AI can help organizations identify potential issues in AI systems, allowing them to implement corrective measures to ward off harm and adverse outcomes. In other words, by knowing how an AI system makes decisions, companies can identify potential risks and take steps to mitigate them. 

In cybersecurity, for example, AI is used to detect potential threats. If an AI system can explain why it’s flagging a certain activity as suspicious, the organization can better understand the threat to its systems and how to address it. With an explainable model, an organization can create a comprehensive security system to protect its data from the worst of attacks.

Responsible and explainable or not, AI is here to stay. Its use in everyday applications is only going to grow, and that means being able to explain what’s going on will continue to be a front-and-center concern.

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About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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