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Choose the simplest model that can meet your application’s required performance, safety, reliability, robustness, security, fairness, privacy, and explanation standards. Start by defining what affected people, decision-makers, reviewers, auditors, and operators must understand and what they need to do with that information. Test an inherently understandable model, such as a decision tree, as your baseline. Consider an opaque model only when it delivers a material, demonstrated benefit and tested, domain-appropriate explanations can satisfy the people who rely on the decision.
Record the selection rationale, evaluation results, explanation tests, human-review process, model version, and later updates. This approach follows the context-based risk management described by NIST, the UK Government AI Risk Management Toolkit, the UK Information Commissioner’s Office (ICO), and the UK Ministry of Justice SAFE-D principles.
Start with the decision and the people affected
Model selection is not an abstract contest between algorithms. It is a decision about how an automated or assisted judgment will affect a person, an organisation, or public safety. Before comparing models, write down the decision the system supports, the action that may follow, and the people who can be helped, harmed, questioned, or denied an opportunity by the result.
Identify every explanation audience
- Affected people: They may need a clear reason for an outcome, information about the factors that mattered, and a practical way to correct inaccurate data or challenge a decision.
- Decision-makers and reviewers: They need enough context to verify the output, identify an error, and decide whether to accept, override, or escalate it.
- Auditors and governance teams: They need evidence that the model, data, controls, and explanations meet the organisation’s requirements over time.
- Operators and engineers: They need to understand system behaviour, monitor changes, investigate incidents, and connect an output to the exact model version that produced it.
Ask each audience what it must understand and what action that understanding enables. A feature-importance chart may help an engineer investigate a pattern, but it is not automatically a complete reason that a person can use to contest a consequential decision.
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Define the explanation you actually need
NIST’s AI Risk Management Framework (AI RMF 1.0, released January 26, 2023) distinguishes three related ideas. Transparency addresses what happened in the system. Explainability addresses how a decision was made. Interpretability addresses why the decision was made and what it means in context for the user. The UK Ministry of Justice SAFE-D principles make a similar distinction between understanding how a technology works and understanding how and why it made a particular decision in a particular situation.
Use those distinctions when writing requirements. For example, an operator may need a system-level account of how inputs flow through a model, while a decision recipient may need a case-specific reason and a route to correct a relevant fact. If you do not specify the audience and purpose first, you cannot tell whether a proposed explanation is adequate.
Set application-specific requirements before comparing models
NIST treats trustworthiness as a combination of validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed. These characteristics must be balanced in the context of use. Predictive accuracy and interpretability can trade off in some settings, so a single score cannot decide the choice for you.
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Set the minimum acceptable level for each characteristic before looking at candidate results. Include:
- Validity and reliability: Define what a correct or useful output means for the task and how consistently the system must perform.
- Safety: Identify foreseeable harm, unacceptable failure modes, and safeguards required before an output can influence action.
- Security and resilience: Specify protection against unauthorised access, manipulation, service failure, and changing operating conditions.
- Fairness: Identify groups that could experience harmful bias and the evidence needed to detect and manage it.
- Privacy: Define limits on collection, use, retention, access, and disclosure of personal information.
- Explanation quality: State who must understand what, at which level of detail, and what review or challenge the explanation must support.
NIST does not prescribe one universal acceptable threshold. Its framework says metrics and threshold values require human judgment in context. Your written thresholds should therefore identify the responsible decision-makers, the evidence they will review, and the consequences of falling below a threshold.
Account for impact and safety criticality
The ICO says the impact of the application and the data being used should inform model choice. In high-impact or safety-critical applications, weigh safety and performance heavily, including accuracy, security, reliability, and robustness. A model that is easier to describe but fails a safety requirement is not an acceptable choice. Conversely, a small performance gain from an opaque model does not justify deployment unless it is material for the decision and its explanations meet the application’s needs.
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Use an inherently interpretable model as the baseline
The UK Government AI Risk Management Toolkit recommends using inherently explainable approaches when possible, naming decision trees as an example. An inherently understandable structure gives you a direct starting point for examining how inputs relate to outputs, rather than relying entirely on a separate explanation tool.
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Test real usability, not just the model label
Calling a model interpretable does not guarantee that every intended user can understand every result. Evaluate the actual structure, number of conditions, terminology, output format, and decision context. A technically simple model can still be confusing if it presents obscure features, conflicting rules, or a level of detail that the audience cannot use.
For each baseline output, ask:
- Can the intended reader identify the main factors that led to this result?
- Can a reviewer check those factors against the underlying record?
- Can the affected person understand what could be corrected or changed?
- Can an auditor reconstruct the path from input data to outcome?
- Does the explanation remain understandable when the model is deployed at the intended scale?
Use the answers to refine the model, the interface, or the operating procedure. Do not treat interpretability as a binary property that ends the evaluation.
Compare candidate models on the same evidence
Evaluate every candidate on the same task definition, representative and appropriately governed evaluation data, operating assumptions, and predeclared criteria. This makes the comparison meaningful and prevents a model from appearing better because it was tested under more favourable conditions.
| Comparison axis | Questions to answer | Evidence to retain |
|---|---|---|
| Predictive performance | Does the candidate meet the task’s minimum validity and reliability threshold? | Task-appropriate evaluation results, error analysis, and the conditions under which they were measured |
| Safety and robustness | How does it behave under foreseeable failure, changed conditions, unusual inputs, or degraded services? | Safety tests, robustness analysis, incident scenarios, and mitigation decisions |
| Security and resilience | Can the system resist unauthorised access or manipulation and continue operating safely when components fail? | Security assessment, resilience controls, and recovery procedures |
| Fairness and privacy | Could the data or model create harmful bias or unnecessary privacy risk? | Group-specific analysis, data-governance decisions, privacy controls, and unresolved limitations |
| Explanation fit | Can the model support the explanation type each audience requires? | Explanation examples, user testing, accuracy and clarity findings, and escalation rules |
| Auditability and operations | Can staff monitor, review, update, and trace outputs to a specific version? | Model cards or equivalent records, logs, version identifiers, update history, and operating procedures |
The cited guidance does not assign universal weights or numerical cutoffs to these axes. Set the weighting and decision rule with the people accountable for the application’s risks, and explain why those choices fit the use case.
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An opaque model should enter the comparison as a challenger to the interpretable baseline, not as the default winner. Establish the benefit it provides over the baseline and test whether supplemental interpretability can meet the explanation requirements.
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Show a material, relevant benefit
Define what benefit matters: for example, a performance improvement that changes the safety margin, a reliability gain that reduces a critical failure, or a capability the baseline cannot provide. Measure that benefit on the same task and conditions used for the baseline. Do not substitute a general claim that the model is more advanced, or a small numerical difference with no practical consequence.
Choose explanation methods for the domain
The ICO advises that an opaque model in a high-impact or safety-critical application should have supplemental tools that provide a domain-appropriate level of explainability. The UK toolkit lists visualization, model extraction, and feature importance as examples of explanation approaches. These methods answer different questions and can fail in different ways, so select them according to the audience and decision being explained.
Before deployment, test whether the explanations are accurate enough for their stated purpose, clear to the intended readers, and useful for review or action. A plausible-looking explanation is not sufficient evidence that it faithfully represents the model’s reasoning. If the explanation cannot support the required challenge, correction, or safety review, the opaque candidate has not met the requirement even if its predictive score is higher.
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The UK Government AI Risk Management Toolkit recommends testing explanation methods and the resulting explanations before deployment. It also recommends gathering feedback from relevant stakeholders and potentially affected people about accuracy, clarity, and understandability.
Build an explanation test set
- Select representative cases. Include ordinary cases, borderline cases, adverse outcomes, unusual inputs, and known risk scenarios. Protect personal information and use appropriately governed data.
- Show the explanation in the form users will receive. Test the actual screen, report, notice, or review workflow rather than an engineering prototype.
- Check factual accuracy. Ask whether the explanation describes the inputs and model behaviour that actually produced the output, within the limits of the method.
- Check clarity and understandability. Ask intended readers to restate the reason in their own words and identify any term or visual they cannot use.
- Check usefulness. Determine whether a reviewer can verify the record, whether an affected person can identify a possible correction, and whether an operator can decide what to investigate.
- Record disagreements and failures. Treat recurring confusion, misleading emphasis, or inability to act as defects requiring redesign, a different explanation method, or a different model.
Stability can also matter. Where the application requires consistent reasons, examine whether small irrelevant changes produce materially different explanations. Faithfulness, stability, and other evaluation questions are practical tests to define for your use; the cited guidance does not set universal pass marks for them.
Design human oversight as part of the system
Human review does not automatically make a model safe or interpretable. Oversight works only when reviewers have the authority, information, training, time, and escalation routes needed to act on what they see.
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Specify the reviewer’s job
- State which outputs require review and which may proceed automatically.
- Give reviewers the input data, model output, explanation, confidence or uncertainty information when appropriate, and relevant policy context.
- Define when a reviewer must override, pause, request more information, or escalate to a specialist.
- Prevent review from becoming a rubber stamp by tracking overrides, disagreement, and reasons.
- Monitor whether workload, deadlines, or interface design make meaningful review impossible.
NIST characterizes trustworthiness as connected to organisational behaviour and interactions with humans. The toolkit also recommends stakeholder engagement. Include the people who operate the system and those potentially affected by it when you design the review process, not only after deployment.
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Document the choice and preserve version traceability
The ICO recommends documenting domain standards, the application’s impact, how data informed model choice, the purpose of any challenger model, and model updates. These records let a decision recipient or investigator understand which version produced a decision.
Maintain a selection record
- The decision, affected groups, explanation audiences, and actions each explanation must support.
- The application-specific thresholds for performance, safety, reliability, robustness, security, fairness, privacy, and explanation quality.
- The interpretable baseline, all compared candidates, evaluation data governance, test conditions, and results.
- The reason an opaque candidate was or was not selected, including the demonstrated benefit and explanation test findings.
- The explanation methods used, their intended limits, stakeholder feedback, unresolved risks, and mitigations.
- The human-review authority, training, escalation path, monitoring measures, and override process.
- Model identifiers, training or release dates, update history, deployment configuration, and the period during which each version was active.
Set review triggers
Revisit the selection when the model, data, decision context, user population, or deployment scale changes. Also trigger a review when monitoring shows drift, new failure modes, fairness concerns, security events, privacy issues, or repeated explanation complaints. A model that was suitable under one set of conditions may no longer satisfy the same thresholds after a material change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply the guidance in the right jurisdiction
NIST AI RMF 1.0 is voluntary, and NIST’s framework page says the framework is under revision. The ICO material is UK data-protection guidance, while the UK Government toolkit and Ministry of Justice SAFE-D principles address UK public-sector risk and responsible model selection. They provide a strong decision structure, but a particular sector or jurisdiction may impose additional legal, regulatory, accessibility, safety, or record-keeping obligations.
Confirm the current status of the applicable framework and obtain sector-specific advice before deployment. Keep the jurisdiction, edition, and date of each requirement in your selection record so reviewers know which obligations were considered.
A practical go/no-go checklist
- Have you named the decision, affected people, decision-makers, reviewers, auditors, and operators?
- Have you stated whether each audience needs to know what happened, how the system decided, why the result matters, or what can be changed?
- Are minimum thresholds written for validity, reliability, safety, security, resilience, fairness, privacy, and explanation quality?
- Was an inherently understandable baseline tested under the same conditions as other candidates?
- If an opaque model is proposed, is its benefit material, demonstrated, and relevant to the application’s risk?
- Have supplemental explanations been tested for accuracy, clarity, understandability, and practical usefulness with relevant stakeholders and potentially affected people?
- Do reviewers have authority, information, training, time, and an escalation route?
- Can every production decision be tied to the exact model version, data context, explanation method, and review record?
- Are there documented triggers for reassessment when the model, data, deployment, or observed impacts change?
What the official guidance says about the core choice
The UK Government AI Risk Management Toolkit states: “When possible, approaches should be utilised that are inherently explainable, such as decision trees.” NIST AI RMF 1.0 explains the distinction this way: “Transparency can answer the question of ‘what happened’ in the system. Explainability can answer the question of ‘how’ a decision was made in the system. Interpretability can answer the question of ‘why’ a decision was made by the system and its meaning or context to the user.”
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Taken together, the guidance supports a disciplined choice rather than a universal model ranking: define the explanation and risk requirements, test an interpretable option, demand evidence for any opaque alternative, involve the people affected, and retain records that make the decision auditable over time.
Frequently Asked Questions
Is an interpretable model always better than an opaque model?
No. The appropriate choice depends on the application’s required performance, safety, reliability, robustness, security, fairness, privacy, and explanation quality. An opaque model should be considered only when it provides a material, demonstrated benefit and tested, domain-appropriate explanations satisfy the stated needs.
What explanation should a person receive after a high-impact decision?
That depends on the decision and audience. Define whether the person needs to know what happened, how the system reached the result, why it matters in context, and what information or action could change or challenge the outcome. Test the explanation with potentially affected people before deployment.
Does human review make an AI system interpretable?
No. Reviewers need authority, information, training, time, and escalation routes, and the model and explanations still require evaluation. Monitor overrides and disagreements to verify that review is meaningful rather than a rubber stamp.
How should model updates be recorded?
Record model identifiers, release or training dates, deployment periods, update reasons, evaluation results, explanation methods, and review outcomes. Preserve enough information to connect each decision to the exact version that produced it.
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