Building trust: transparency in AI decision-making

Transparency in AI decision-making means that people know when an automated system was involved in a decision about them and can learn the main factors behind the outcome, and that a regulator or researcher can verify that the system behaves as claimed. When an AI system shapes a decision about a loan, a diagnosis or a job application, the people affected deserve to know how it was reached and be able to contest it.

What transparency means, and what it does not

Transparency has four layers:

• Disclosure: a person knows that an automated system was involved in a decision about them.
• System transparency: documentation of the system’s purpose, training and test data, performance for different groups, and when it should not be used.
• Explainability: the main factors behind a particular outcome for a particular person.
• Auditability: records and evidence that let a regulator or researcher verify that the system behaves as claimed.

Transparency does not mean publishing source code or model parameters; a file of model weights tells a refused applicant nothing. The goal is information its audience can act on.

Why oversight and redress depend on it

Each layer protects a capability: to ask whether a decision was automated, to spot and correct a wrong input such as a misrecorded payment, and to establish later whether the system worked as intended.

European law builds on this. Article 22 of the GDPR gives individuals the right not to be subject to a decision based solely on automated processing with legal or similarly significant effects. Where such a decision is permitted because it is necessary for a contract or rests on explicit consent, the controller must provide safeguards, at least the right to human intervention, to express one’s point of view and to contest the decision.

The EU AI Act (Regulation (EU) 2024/1689) goes further for high-risk systems, which include credit scoring of natural persons, recruitment and access to education. Providers must document such systems, enable logging and make their output interpretable for deployers. Deployers must inform people that such a system is used in decisions about them and assign human oversight to people with the competence, training and authority to intervene. A person subject to such a decision with legal or similarly significant effects that they consider adverse may obtain from the deployer a clear and meaningful explanation of the system’s role in the decision and its main elements (Article 86). Most high-risk obligations were originally due to apply from 2 August 2026, and those for high-risk AI in regulated products from 2 August 2027; the digital omnibus on AI (Regulation (EU) 2026/1744), in force since 27 July 2026, moved these dates to 2 December 2027 and 2 August 2028 respectively.

What a meaningful explanation looks like

For the person affected, an explanation they can act on covers:

• That an automated system was used, and for which part of the decision.
• The main factors that drove the outcome, in plain language.
• What would need to change for a different outcome, where realistic.
• How to reach a human, correct the data and contest the result.

A loan refusal that says “Your application did not meet our criteria” explains nothing. One that says “The main reasons were income below the level required for this loan and two payments recorded as late last year; you can ask for a review of your payment record and a human reassessment” gives the applicant something to check and a route to act. It does not reveal the model; it reveals the decision.

A patient should know that software supported an assessment and that a clinician remains responsible; the clinician needs to know the system’s confidence and where it performs poorly. A job candidate should learn that a screening tool was used, what it assessed and how to request human review, so that a shortlist remains a recommendation, not a verdict.

Methods and their limits

Two technical routes exist: models interpretable by construction, such as scorecards or decision rules, and post-hoc methods for complex models, including feature attribution (such as SHAP and LIME) and counterfactual explanations, which describe the smallest input change that flips the outcome.

Post-hoc methods summarize or approximate a model’s behavior rather than reveal its mechanism; methods can disagree about the same decision, attributions can shift with small input changes, and a fluent explanation can inspire more confidence than the model deserves. Counterfactuals should be limited to changes a person can make: telling someone they would have been approved had they been ten years younger is not advice but a sign that the model relies on a characteristic they cannot change, possibly one that equality law protects.

Trade secrets, other people’s personal data and the risk of gaming can justify some limits; a workable answer is layered access: affected people get what they need to contest their case, auditors get documentation and logs, the public gets summaries of purpose, testing and safeguards.

Evidence, not declarations: a practical takeaway

Public trust rests on evidence that can be checked, not on statements of principle. Human oversight is only real if the people exercising it can overrule the system; the AI Act requires that overseers of high-risk systems be able to stay alert to automation bias, the tendency to rely automatically on a system’s output, and to override it. Anyone who procures, operates or is subject to such a system can ask:

• Whether an AI system is involved, and whether affected people know.
• What the system is meant to do, and where it is known to fail.
• What it was tested on, and the results for different groups.
• Who can override it, and whether they have the time and authority.
• How a person obtains an explanation and contests the outcome.
• Whether decisions are logged for later review.

If an organization cannot answer, the problem is not a complicated model but a system deployed without the conditions for accountability. The Foundation’s work here is research and publishing: on explainability, accountability for automated decisions, and when test results support claims of fairness, which its technical report on bias detection under the EU AI Act examines for synthetic, anonymized and differentially private data.

In short

AI transparency means people know when an automated system was involved in a decision about them and can learn the main factors behind the outcome, while regulators and researchers can verify the system behaves as claimed. It has four layers: disclosure, system transparency, explainability and auditability. The goal is information its audience can act on, not published source code.

Frequently asked questions

What is the difference between transparency and explainability in AI?

Transparency is the broader idea and has four layers: disclosure, system transparency, explainability and auditability. Explainability is one of those layers. It covers the main factors behind a particular outcome for a particular person.

Does AI transparency mean publishing the source code?

No. AI transparency does not mean publishing source code or model parameters, because a file of model weights tells a refused applicant nothing. The goal is information that its audience can act on.

What does GDPR Article 22 say about automated decisions?

Article 22 of the GDPR gives individuals the right not to be subject to a decision based solely on automated processing with legal or similarly significant effects. Where such a decision is permitted because it is necessary for a contract or rests on explicit consent, the controller must provide safeguards, at least the right to human intervention, to express one’s point of view and to contest the decision.

Does the EU AI Act give people a right to an explanation of an AI decision?

For high-risk systems, the EU AI Act (Regulation (EU) 2024/1689) provides that a person subject to a decision with legal or similarly significant effects, which they consider adverse, may obtain from the deployer a clear and meaningful explanation of the system’s role in the decision and its main elements (Article 86). High-risk systems include credit scoring of natural persons, recruitment and access to education.

What makes an explanation of an automated decision meaningful?

A meaningful explanation says that an automated system was used and for which part of the decision. It gives the main factors behind the outcome in plain language, says what would need to change for a different outcome where realistic, and explains how to reach a human, correct the data and contest the result.

How to cite LASTHUMAN Foundation. (2026, September 15). Building trust: transparency in AI decision-making. https://lasthuman.today/insights/transparency-in-ai-decision-making.html