Explainable AI (XAI): Transparent AI for Compliance

Why explainability is needed

Many strong models are so-called black boxes. Examples are deep neural networks and large tree ensembles. Their millions of parameters do not tell you why a given input leads to a given result. That is fine when a model only suggests products. But it raises questions as soon as the model makes decisions about people or acts in safety-critical processes:

• Was a loan request turned down for sound reasons?
• Has the model learned features it must not use when it pre-screens job seekers?
• Does quality control spot the defect, or just image features that happen to go along with it?

So explainability serves several goals at once:

• debugging and quality checks during development
• finding bias
• real human oversight in operation
• meeting legal duties to inform affected people

Key methods

Interpretable models

Some models are easy to understand by design. These include linear and logistic regression, small decision trees, rule-based systems and generalized additive models (GAMs). For many tables of business data, they work about as well as complex models. If that is the case, an interpretable model is usually a safer choice than an explanation added after the fact.

Post-hoc explanations

• SHAP (SHapley Additive exPlanations): Splits the share of a prediction across the input features. It is based on Shapley values from game theory. It works for local explanations of single decisions. Added up, it also gives a global overview.
• LIME (Local Interpretable Model-agnostic Explanations): Mimics the model around one specific input with a simple stand-in model.
• Counterfactual explanations: Answer the question "What would have had to be different for the result to change?" For example: "With an income X higher, the request would have been approved." Affected people often find these the easiest to grasp.
• Saliency methods such as Grad-CAM or Integrated Gradients: Highlight the parts of an image or text that had the most effect on the result.
• Global methods: Permutation feature importance or partial dependence plots show which features the model relies on overall.

Limits and risks of XAI

Post-hoc explanations are only estimates. They can look plausible and still not show what really goes on inside the model. Different methods can give different explanations for the same decision. Research has also shown that explanation methods can be fooled on purpose.

Take extra care with large language models. A reason the model writes about itself is not always its true decision path. Detailed explanations can also help attackers copy or evade a model. So choose with care how deep an explanation goes and who gets to see it.

Regulatory requirements

• EU AI Act: High-risk AI systems must be built so that deployers can read their output and use it in the right way (Article 13). They must allow real human oversight (Article 14). Article 86 gives affected people a right to a clear and meaningful explanation of certain decisions. This covers decisions based on high-risk systems listed in Annex III. After the Digital Omnibus (in force since 27 July 2026), high-risk duties apply from 2 December 2027 for Annex III systems. For AI in Annex I products, they apply from 2 August 2028.
• GDPR: For automated decisions under Article 22, Articles 13 to 15 require meaningful facts about the logic involved.
• Standards and frameworks: ISO/IEC 42001 and the NIST AI Risk Management Framework treat transparency and explainability as traits of trustworthy AI.

Practical approach

1. Know your audiences: Data scientists need different explanations than case handlers, auditors or customers.
2. Justify the model choice: Before you use a black box, check whether an interpretable model is enough. Write down the result.
3. Test explanation methods: Check that explanations are stable and plausible. For example, compare several methods or bring in subject experts.
4. Build explanations into the process: Case handlers should see the explanation when they decide. They should be able to overrule the AI if they give a reason.
5. Document: In the technical documents, record the model's purpose, the factors that shape it, its limits and its known weak spots.

Conclusion

Explainability is not a single tool. It is a design choice that runs from picking the model to the case handler's screen. SecTepe helps you build transparency rules into AI governance and risk management.

Frequently asked questions

Does every AI system need to be explainable?

No. Explainability is mainly a legal duty for high-risk systems and automated decisions about people. For internal quality checks, though, it is almost always worth it.

Is it enough to tell an affected person the most important features?

A list of abstract features is often not enough. Explanations are easier to understand if they refer to the specific case. They should show which factors shaped the result the most.