Access to technical detail does not guarantee that a person can understand or challenge an automated decision. An organization may publish source code, a model card, or an accuracy score and still leave an affected person unable to learn why a benefit was denied, what information mattered, or how to correct an error. That gap separates transparency from explainability.

Transparency makes relevant facts about a system visible. Explainability makes a particular output or process intelligible to a particular audience for a particular purpose. The two support each other, but neither substitutes for reliable evidence, responsibility, or an effective route to appeal.

Transparency Describes The System

Useful transparency identifies who develops and operates a system, its intended use, data sources, known limitations, evaluation results, update history, and oversight arrangements. It also discloses when someone is interacting with AI and when automated output materially influences a decision.

Different audiences need different layers. Procurement officials need supplier and testing records. Researchers need methodological detail. Frontline workers need operating limits and escalation rules. Members of the public need plain-language notice about purpose, consequences, and rights. Publishing everything in one technical repository can create formal openness without practical access.

Explainability Answers A Relevant Question

An explanation should begin with the recipient’s need. A person denied a service may ask which facts drove the result and what can be corrected. A clinician may ask whether a recommendation fits the patient before them. An engineer investigating drift needs feature behavior, data lineage, and version history. One generic explanation cannot serve all three.

The National Institute of Standards and Technology proposes four principles for explainable AI: a system should provide an explanation, make it meaningful to the recipient, accurately reflect the process used, and recognize the limits of its knowledge.[1] A plausible story is not enough; an explanation can sound clear while misrepresenting what the system actually did.

Global And Local Explanations Serve Different Purposes

A global explanation describes how a system generally behaves: important variables, decision logic, performance conditions, and common failure modes. A local explanation addresses one output. A local reason without system context can hide a defective policy, while a global summary may not help a person understand an individual outcome.

Feature-importance charts, examples, simplified rules, and counterfactuals can help, but each has limits. A statement that approval would change if income were higher may describe a model boundary without explaining whether the criterion is lawful, accurate, or fair. Explanation tools should be tested for fidelity, stability, and comprehension rather than selected for attractive graphics.

More Disclosure Is Not Always Better

Transparency can conflict with privacy, cybersecurity, intellectual property, and prevention of gaming. Publishing personal training records is unacceptable. Revealing exact fraud thresholds can make abuse easier. A responsible approach discloses enough for scrutiny and recourse while protecting legitimate secrets and sensitive information.

Confidentiality should not become a blanket excuse. Auditors, regulators, courts, or qualified researchers may receive deeper access under safeguards when public disclosure is inappropriate. Aggregate performance, system purpose, decision criteria, and appeal procedures can often be public even when code or data cannot.

Explanation Must Connect To Evidence

A system may be easy to describe yet unreliable. Explanation should accompany evidence about validity, calibration, robustness, subgroup performance, and real-world outcomes. The NIST AI Risk Management Framework places transparency and explainability within a wider set of trustworthy characteristics that includes validity, safety, security, privacy, accountability, and fairness.[2]

The intended decision matters. A rough explanation may be adequate for recommending a song but not for determining employment, health care, credit, or public benefits. Higher stakes demand stronger evidence, clearer records, qualified review, and a way to pause use when harm emerges.

Notice Should Arrive Before Consequence

People should know when AI is materially involved before they rely on or respond to its output. Notice should identify the operator, purpose, type of data used, nature of human involvement, important limitations, and contact for questions. It should not force people to infer automation from a privacy policy.

The OECD AI Principles connect transparency and explainability with awareness, understandable information about AI interactions, and information enabling people adversely affected by an output to challenge it.[3] Explanation has practical value when it changes what a person can do next.

Recourse Completes The Explanation

A statement of reasons without correction or appeal can merely explain power. People need a channel to dispute inaccurate inputs, submit context, request human review, and receive a timely response. Reviewers need authority to change the outcome, not only confirm that software was followed.

Records should connect each consequential output to the model version, inputs, thresholds, human actions, and final decision. The European Union AI Act requires transparency information for high-risk systems and provides, in defined circumstances, a right to obtain clear and meaningful explanations about their role in individual decisions.[4]

Test Explanations With Real Users

Comprehension cannot be assumed. Organizations should test whether intended users can identify what an output means, what it does not mean, which factors mattered, and how to contest it. Testing should include people with disabilities, limited technical knowledge, different languages, and frontline workers expected to act on the system.

An explanation that is faithful but incomprehensible fails its audience. One that is understandable but inaccurate fails the truth. Teams should measure both, document tradeoffs, and revise explanations when behavior, policy, or user needs change.

A Practical Disclosure Structure

Organizations can organize information into six layers:

  1. Notice: State that AI is being used, by whom, and for what purpose.
  2. System Facts: Describe data, intended use, limitations, performance, and updates.
  3. Decision Reasons: Explain material factors behind a consequential output.
  4. Evidence: Provide relevant validation, subgroup, robustness, and monitoring results.
  5. Responsibility: Name the people and institutions accountable for operation and review.
  6. Recourse: Offer correction, appeal, human reconsideration, and remedy.

Clarity Must Lead To Accountability

Transparency enables scrutiny. Explainability helps a person or professional interpret and respond to a system. Both fail when they become communication exercises detached from authority, evidence, and remedy.

A trustworthy system does not simply reveal that automation exists. It makes important facts accessible, gives reasons suited to the decision, admits uncertainty, and leaves a usable path for challenge. Understanding should increase agency, not merely make an unchangeable result easier to describe.

Different audiences need different forms of evidence and recourse. Our guide to auditing AI in public decisions addresses independent scrutiny, while responsible AI beyond compliance connects disclosure to ownership and correction.

Sources

  1. National Institute of Standards and Technology, Four Principles Of Explainable Artificial Intelligence.
  2. National Institute of Standards and Technology, AI Risk Management Framework 1.0.
  3. Organisation for Economic Co-operation and Development, OECD AI Principles.
  4. European Union, Regulation 2024/1689 Laying Down Harmonised Rules On Artificial Intelligence.