What is explainable AI? How XAI works and why it matters

Explainable AI — also known as XAI (explainable artificial intelligence) — is key to understanding how AI systems make decisions. This discipline aims to make the processes used by complex algorithms transparent and accessible so that both experts and users can understand, trust, and verify the results they generate. Explainable AI not only builds trust among users across multiple sectors but also ensures greater responsibility, fairness, and safety in its application.

In this post, we explore the importance of explainable AI and how it’s shaping the future of technology. We’ll also look at how to apply it, along with the benefits and challenges it brings.

What is explainable AI?

Explainable AI is a branch of artificial intelligence focused on making the internal processes of algorithms understandable and transparent. It encompasses a set of techniques designed to produce results in addition to providing explanations that can be interpreted by people without expertise in programming or statistics.

Unlike traditional AI, which doesn’t reveal the reasoning behind its decisions, explainable AI enhances user trust by showing how and why the system arrives at certain outcomes. In its AI principles, the Organization for Economic Cooperation and Development (OECD) notes that explainability is crucial “to foster a general understanding of AI systems, including their capabilities and limitations.” According to the OECD, “where feasible and useful,” AI developers should “provide plain and easy-to-understand information on the sources of data/input, factors, processes, and/or logic that led to the prediction, content, recommendation, or decision, to enable those affected by an AI system to understand the output.”

How to apply explainable AI

Implementing explainable AI involves adopting practices and methodologies that make AI models’ operations understandable and transparent. These are some key steps to applying it:

• Choose interpretable models. Whenever possible, use AI models that are inherently interpretable, such as decision trees or simple rule-based systems. When the complexity of a task requires more advanced models — like deep neural networks — it’s essential to pair them with explainability techniques to clarify and justify their outputs.
• Visualize results and reasoning. Present explanations in ways that are clear to both experts and non-experts. This can be in the form of charts, summaries, or textual descriptions showing how the model reached a particular conclusion.
• Document and maintain transparency. Keep detailed records of model development, the data used, the most important variables, and the rationale behind decisions.
• Continuously evaluate explainability. Regularly review whether the explanations generated are effective and understandable for users.

These principles make AI a more reliable, collaborative, and ethical tool, fostering adoption in sectors such as healthcare, finance, justice, and government.

In logistics, applying explainable AI helps teams understand system decisions — critical for processes like inventory management and warehouse resource allocation. For example, an AI platform might recommend reorganizing pallet locations to speed up order picking and shipping. With explainable AI, warehouse managers not only receive the recommendation but also see which data informed it (e.g., picking frequency, proximity to loading docks, or SKU volumes). This transparency builds trust in the system and allows managers to validate critical operational decisions.

Benefits of explainable AI

Adopting explainable AI brings numerous advantages for both organizations and society:

• Trust in technology. Users can understand how results are generated, reducing the sense of a black box and building confidence in the system.
• Regulatory compliance. XAI supports compliance with emerging US legislation and frameworks for algorithmic transparency, fairness, and accountability.
• Error and bias detection. It helps identify model flaws, mitigate risks, and increase prediction accuracy.
• Continuous improvement. By revealing which variables most influence outcomes, explainable AI enables ongoing system optimization.
• Informed decision-making. Stakeholders receive not just a result but also an explanation that helps them understand context and justify their decisions.

Ultimately, explainable AI enhances trust and transparency while promoting higher quality and accountability in the use of artificial intelligence.

What’s the difference between explainable AI and interpretable AI?

The main difference between explainable AI (XAI) and interpretable AI lies in their focus and scope.

Interpretable AI refers to models that are inherently understandable. Their inner workings are transparent and easy to follow. Typical examples are decision trees and linear models, where it’s possible to see how each variable affects the outcome.

Explainable AI, on the other hand, encompasses both interpretable models and more complex models that are not directly understandable, such as deep neural networks. In these cases, simply looking at the model isn’t enough. Additional techniques are needed to generate clear explanations of how a decision or recommendation was reached, even when the learning system itself is opaque. Tools like SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), and various visualizations help illustrate the relationships between variables and outcomes.

In short, all interpretable AI can be considered explainable, but not all explainable AI is interpretable. Explainability also includes methods that clarify how complex black-box models function.

Challenges in implementing explainable AI

Although XAI offers multiple benefits, putting it into practice isn’t without hurdles. These are some of the biggest challenges:

• Technical complexity. Breaking down advanced systems like deep neural networks takes heavy computing power and specialized expertise.
• Balancing accuracy and transparency. The most precise algorithms aren’t always the easiest to interpret. Finding the right trade-off depends on the goal at hand.
• Handling sensitive data. Providing clear explanations while safeguarding privacy and data security is an ongoing challenge.
• Organizational resistance. Adopting explainable AI often means changing processes and reshaping company culture — where efficiency sometimes gets prioritized over transparency.
• Lack of clear standards. With regulations still evolving, it’s not always obvious how much explainability each industry will require.

Explainable AI: Transparency as a competitive advantage

Explainable AI offers a way to bring greater clarity to AI systems. Its ability to build trust and support regulatory compliance makes it a compelling option for industries. In logistics, for example, the real value of explainable AI lies in providing verifiable explanations for processes where efficiency and traceability are paramount.

But the impact of explainable AI goes beyond technology. It also shapes organizational culture and business practices, allowing clients, auditors, and operations managers to understand and validate algorithmic recommendations. In industries like logistics, healthcare, and finance, explainable AI can make the difference between simply accepting results or thoroughly vetting them. It helps strengthen both competitiveness and trust in critical processes.