What is explainable AI (XAI)?

Explainable AI (XAI) is an approach to AI system development that ensures users understand how an AI model works. Explainable AI methods describe the model and its decision-making process. Explainability transforms a black-box model that delivers unexpected results into a reliable solution people can trust.

Explainable AI is often used alongside responsible AI and trustworthy AI. While all the approaches aim to make AI systems more transparent, they focus on different aspects. Explainable AI (XAI) makes AI decisions understandable to humans and is among the core requirements for implementing responsible AI. Responsible AI helps prevent bias and discrimination through fairness and accountability. Trustworthy AI ensures compliance with laws and ethical standards.

Why do we need explainable AI (XAI)?

The lack of transparency into how algorithms make decisions leads to unreliable AI systems, discouraging people and companies from using them. 12% of companies name poor explainability among the key risks associated with AI technology. Explainable AI eliminates this issue by making the AI logic straightforward and bringing the following benefits:

• Trust and adoption by users. Only 51% of users trust businesses to use gen AI responsibly, according to the recent Deloitte report. The benefits of explainable AI enable people to understand how the system works, making them more likely to rely on its results.
• Regular model improvements. Detailed documentation on how explainable AI works helps developers identify errors in model operation and speeds up debugging. It improves the overall accuracy and quality of AI predictions.
• Regulatory compliance and auditability. Traceability of explainable AI software simplifies compliance with regulations such as GDPR. Regulatory bodies can check how an organization processes data and ensure users are informed about the factors that influence AI results.
• Bias detection and ethical assurance. Since engineers can track what determines the outcome, they easily detect model bias and make the necessary changes to prevent discrimination.
• Safety in high-stakes domains. Organizations in healthcare, finance, lending, or other regulated fields must ensure that AI decisions are verifiable.

Key Explainable AI Principles

The explainable AI definition implies four core principles: clarity, transparency, accountability, and consistency. They guide the development of an AI-powered system and ensure the implemented model is easy to understand and reliable for end-users.

Principle	Description	Explainable AI Examples in Industries
Clarity	Reasons behind AI decisions are clear	Claim assessment tool provides an explanation of why the claim was denied
Transparency	Decision logic is documented	Radiology software highlights the factors that influenced the offered diagnosis
Accountability	Assigned responsibility for AI output	Audit logs in manufacturing equipment allow for accident investigations
Consistency	AI model behaves predictably and relies on the same criteria	Loan approval system approves the same categories of loans based on specific criteria

Interpretable AI vs. Explainable AI

Explainability and interpretability both make AI systems easier to understand. The key difference is that interpretability focuses on clarifying HOW an AI model works, while explainability is a broader concept that describes WHY a black-box model makes specific decisions. Here's a summary:

• Interpretable AI includes the methods that ensure AI models are inherently understandable by design.
• Explainable AI requires post-hoc methods to explain complex, black-box models.

Criteria	Interpretable AI	Explainable AI
Model type	Simple models	Black-box, complex models
Understanding	Immediate, no extra tools required	Specialized explanation methods and tools required
Examples	Linear regression, decision trees, rule-based models	Deep neural networks, random forests
Use cases	Regulated or high-stake uses	Performance-critical tasks

Common Explainable AI Techniques and Methods

Explainable AI implementation requires different techniques, depending on the software type and its uses. Here are the main categories of methods and when to apply them:

• Model-specific (LIME, SHAP, Grad-CAM, attention mechanisms, and feature visualization) are tailored to the type of model, breaking down how it works.
• Model-agnostic (feature importance, counterfactual explanations, PDPs) work across various models, measuring how each component affects the output. 
• Visualization and reporting tools help present explanations to human users.

Cooperating with an engineering company that offers explainable AI (XAI) services helps build systems that comply with XAI standards. Third-party engineers can assist with XAI-compliant development or provide ethical AI consulting to train an in-house engineering team on the best practices. It facilitates explainable AI implementation and may be an optimal approach for tech companies that lack in-house expertise.

Real-World Use Cases of Explainable AI (XAI)

Explainable AI implementation is crucial when a company works in a regulated industry, such as healthcare and finance, or when AI decisions directly impact human lives. Common explainable AI examples include:

• Automotive manufacturers use saliency maps to identify which objects determine the vehicle's decision to slow down.
• Explainable AI tools like Grad-CAM reason the diagnosis by highlighting suspicious regions in radiology scans.
• Financial organizations use Explainable AI to clarify credit approval or denial decisions for regulatory compliance.

Takeaways

Knowing what is explainable AI (XAI) is essential for companies that want to remain regulatory-compliant and build user trust. Explainable AI is one of the leading AI adoption trends, along with ethical, responsible, and trustworthy AI. This combination of tools and approaches ensures human users understand how the model works and what affects its decisions. It makes people more likely to use AI systems and rely on their output, accelerating AI adoption.