One of the great dilemmas of today's artificial intelligence is its "black box" nature. Models generate answers, recommendations or predictions with a high degree of accuracy, but it is often impossible to know how they reached that conclusion. In the corporate sphere, where trust, security and regulatory compliance are fundamental, this lack of transparency represents a real risk. The relevant questions are: what data was the model trained on? Which sources did it consult? What logic did it follow to give that answer?
Explainable AI offers verifiable answers to those questions. It does not seek to make systems simpler, but to make them understandable: so that their reasoning can be followed, audited and, if necessary, corrected.
Why is transparency key?
There are four specific reasons why transparency in AI has stopped being an ideal and become a requirement.
- User trust. According to an analysis by Bismart (2025), citing Forrester data, global consumer trust in AI has fallen from 61 % to 53 % over the last five years, and 60 % of companies using AI acknowledge trust issues with their own models. Customers and employees trust a tool more when they understand how it reaches its conclusions.
- Regulatory compliance. Regulation (EU) 2024/1689, known as the EU AI Act, is the world's first comprehensive legal framework on artificial intelligence. It entered into force on 1 August 2024, its prohibitions have been enforceable since 2 February 2025 and it will be fully applicable to high-risk systems on 2 August 2026. For those systems, the Regulation requires traceability of the data used, activity logs and a sufficient level of transparency to allow deployers to interpret the results. Non-compliance can lead to penalties of up to 35 million euros or 7 % of total annual worldwide turnover. In Spain, the Spanish Agency for the Supervision of Artificial Intelligence (AESIA) is the body responsible for enforcing these rules.
- Corporate accountability. Faced with a critical decision such as granting a loan, recommending a medical treatment or selecting a candidate, the company must be able to justify and audit why the AI acted in a certain way. Without that capability, accountability is left up in the air and legal exposure is high.
- Bias prevention. Explainability makes it possible to identify and correct discriminatory patterns or systematic errors in training data before they have consequences in real decisions.
How to build auditable and traceable systems
Implementing explainable AI requires an approach that combines technology and organisation. There are five elements that cannot be missing.
1. Define a governance framework
Technology alone is not enough: the company needs an organisational framework that establishes who oversees the AI, how decisions are documented and how incidents are managed. Article 17 of the AI Act requires providers of high-risk systems to put in place a quality management system equivalent, in structure, to the controls of the ISO 9001 standard.
2. Use proven explainability techniques
There are two widely adopted methods for interpreting complex models:
- LIME (Local Interpretable Model-Agnostic Explanations): explains individual predictions by showing which variables determined them.
- SHAP (SHapley Additive exPlanations): developed from cooperative game theory, it quantifies the contribution of each variable to the overall result.
Both techniques can be applied to models that have already been trained, without the need to replace them. A complementary alternative is hybrid models, which combine neural networks with explicit logical rules to increase clarity without sacrificing accuracy.
3. Build traceability into the design
Every interaction with the AI should generate an auditable record: what data was used, which version of the model was employed and how the answer was reached. When the system uses external information retrieval, as happens with RAG (Retrieval-Augmented Generation) architectures, the record should also indicate which documents or databases were consulted.
4. Ensure source transparency
Technologies such as MCP (Model Context Protocol) allow systems to indicate not only the answer, but also which database or API they consulted to obtain it. This turns explainability from an ideal into a concrete technical mechanism.
5. Keep a human in the loop
For decisions with a direct impact on people, the AI should act as an assistant, not as the final arbiter. An expert must validate, question or correct the model's recommendation before it has real effects.
From myth to practice: explainable AI in action
XAI is not an academic concept. It is already operational in sectors where algorithmic decisions have direct consequences for people.
- In finance, a credit scoring system must justify which variables influenced the decision to approve or reject an application. The AI Act classifies these systems as high-risk and requires explainability, human oversight and periodic auditing.
- In healthcare, a clinical decision support system must show which medical evidence or studies it used to recommend a treatment. Without that traceability, the doctor's accountability is compromised and the tool cannot legally be integrated into critical decision-making workflows.
- In HR, an assistant that helps screen candidates must record which criteria it applied and how it weighted them. Since February 2025, companies that use AI in recruitment processes must ensure that their staff have a sufficient level of knowledge of those tools, an obligation set out in the AI Act regardless of the size of the organisation.
Explainability turns AI into a trustworthy partner, not an unquestionable oracle.
At LurNova we are clear about it
At LurNova we design artificial intelligence solutions for corporate environments where trust, auditing and regulatory compliance are non-negotiable. Our approach integrates:
- Answer traceability (what data was used and how).
- Source transparency through RAG and MCP.
- Auditable records to facilitate compliance with the AI Act.
Because the real value of AI is not only what it answers, but the trust it generates by being able to explain why it does so.
CEO of LurNova, with extensive experience in technology and digital transformation. Involved in developing technology projects and applying artificial intelligence in organisations, he combines technical knowledge with business vision.
