Why SHAP explainability does not prove fairness

How to study this topic

SHAP explainability can help a bank understand feature contribution, but it does not prove fairness because explaining why a model produced an output is not the same as proving the output is lawful, unbiased, stable, accurate or acceptable for customer impact. Study this as a banking governance chapter. The important question is not whether AI can produce a score, explanation or document pack. The important question is whether the bank can prove the model is suitable, lawful, monitored, limited and accountable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The topic belongs to explainability limits, fair lending and banking model validation. Keep the focus on credit, compliance, model-risk governance, fair lending, customer outcome, validation evidence and approval control. Do not drift into generic AI productivity language. In banking, the model output matters only when the control context around it is strong enough.

A strong learner should be able to explain this topic to a credit-risk manager, fair lending specialist, compliance analyst, model validator, product owner, developer, tester, auditor and governance committee member. Each person should understand what evidence is needed, what the model can do, what it cannot prove and where human accountability remains.

Plain language meaning

In plain language, why shap explainability does not prove fairness is about preventing AI from being treated as trusted banking evidence before the bank has tested the model, checked the law, reviewed the customer impact and agreed the approval boundary. A model can be technically impressive and still be unsuitable for a regulated credit or compliance use.

The practical discipline is to separate prediction, explanation, compliance evidence and final action. Prediction estimates an outcome. Explanation describes model drivers. Compliance evidence proves the bank followed the right control process. Final action affects a customer, report, control, case or policy position. Mixing those four layers is where many model-governance failures begin.

A good bank does not approve AI because it sounds modern. It approves a controlled use of a specific model for a specific population, purpose, product, jurisdiction and decision boundary. That approval should be visible in the model inventory, validation pack, committee record and monitoring process.

Where it sits in the bank

This topic normally sits across credit risk, compliance, fair lending, legal, model risk management, product, data, technology, operations and internal audit. The exact operating model differs by bank, but ownership must not be vague. Someone must own the model, someone must validate it, someone must approve use and someone must monitor the outcome.

The population in scope is credit applicants, declined customers, model validators, fair lending teams, compliance reviewers, data scientists, product owners, complaint teams and governance committees reviewing model explanations. Testing must reflect the population actually affected by the model. Clean demonstration examples are not enough. Banking populations include missing data, thin files, local policy exceptions, manual overrides, legacy records, vulnerable customers, new products, rejected applicants and changing economic conditions.

The output may support application scoring, limit setting, pricing, referral, adverse action notices, compliance review, model approval, documentation review, audit evidence or regulatory response. The risk level changes when the same model moves from research to decision support, then from decision support to automated action.

Evidence and source material

Relevant evidence includes SHAP values, feature contribution, local explanation, global explanation, reason code, protected-group outcome, proxy variable, training sample, baseline population, model version, explanation method, validation test, customer notice, and manual reviewer note. Evidence must be current, traceable and fit for the question being asked. A policy document, model metric, explanation output, validation report or approval note is useful only when the bank can show source, version, owner, date, limitation and approved use.

For credit and fair lending topics, evidence also needs customer-outcome context. A model that performs well at portfolio level can still create unacceptable outcomes for a subgroup, a product segment, a pricing path or a manual referral population. Aggregate accuracy does not remove the need for segment-level challenge.

For model approval topics, evidence must connect the business purpose to the technical method. A validator should be able to see why the model was built, what data it uses, how it was tested, what limitations remain, what risks were accepted and how those risks will be monitored after release.

Control expectations

Controls should include explanation-method validation, reason-code mapping, fairness testing, proxy-variable review, stability testing, baseline sensitivity review, human interpretability check, customer-language review, legal review, model limitation disclosure, monitoring, and override feedback. These controls make the difference between a useful model and an uncontrolled model. The bank should know what must be documented before use, what must be validated independently, what must be approved by governance and what must be monitored in production.

Control design should be proportionate to materiality. A low-risk internal research tool does not need the same approval pack as a model that affects credit access, pricing, limits, regulatory reporting or customer communication. But once the model can influence a material banking outcome, casual governance is not enough.

Controls should also define the response when something is wrong. That may mean restricting use, forcing manual review, changing thresholds, updating reason codes, remediating documentation, retraining users, opening an issue, notifying a committee or stopping the model until the gap is closed.

How AI and ML can be adopted

Useful AI adoption includes summarising feature drivers, supporting reason-code generation, flagging explanation instability, comparing local and global drivers, finding proxy-feature concerns, and helping reviewers challenge model logic. These are support uses first. AI can help find weak documentation, monitor patterns, summarise validation packs, detect proxy risk, compare outcomes and prepare governance material. It should not quietly replace the bank's legal, compliance, validation or approval judgement.

A sensible adoption path starts with controlled analysis and documentation support, then moves into validated decision support, then into restricted automation only when governance, monitoring, fallback and accountability are mature. The higher the customer or regulatory impact, the stronger the approval boundary must be.

The bank should write the model's role in operational language: score, explain, classify, recommend, refer, approve, decline, price, notify, document, monitor or escalate. Each verb carries a different control burden. If the bank cannot name the verb precisely, it cannot govern the use precisely.

Validation and challenge

Validation should review concept, data, methodology, assumptions, implementation, outcome quality, limitations, fairness, explainability, operational use and monitoring design. For banking AI, validation is not a final signature at the end. It is a structured challenge to whether the model is fit for the stated purpose.

Effective challenge means the validator can question the developer, the business owner, the data source, the training sample, the feature logic, the testing design, the reason-code mapping, the customer impact and the proposed monitoring thresholds. Challenge should be documented, answered and closed with evidence.

A model may pass technical accuracy tests and still need restrictions. It may be acceptable for analyst prioritisation but not for automatic decline. It may be acceptable for one product but not another. It may be acceptable in one jurisdiction but not another. Validation should make those boundaries visible.

Customer, compliance and conduct impact

The direct wrong outcome is the bank mistakes a neat explanation chart for proof of fairness, then misses discriminatory outcomes, weak reason codes, unstable explanations or customer-impact problems. That is why this topic should be studied as customer-impact control, not only model governance theory. Credit AI can affect access, price, limit, explanation, complaint handling and trust. Compliance AI can affect evidence, escalation and regulatory position.

A bank must ask who is affected when the model is wrong. Is a sustainable applicant declined? Is an unaffordable customer approved? Is a protected group disadvantaged? Is a reason code inaccurate? Is a reviewer over-trusting the explanation? Is a governance committee approving a model without seeing a key limitation?

Conduct risk appears when the model creates pressure, exclusion, opacity, delay, poor explanation or weak remediation. The bank should treat those outcomes as control issues, not as cosmetic issues in the user interface or documentation wording.

Diagram walkthrough

The diagram follows five control steps: Model output, SHAP explanation, Fairness tests, Reason-code review, and Governance decision. Read it left to right. It starts with the model or regulatory use case, moves through testing and governance, and ends with accountable use and retained evidence.

Each box is a bank control point. The implementation should name the owner, input, rule, evidence, review point and limitation at every step. If one box cannot be explained clearly, the model is not ready for high-trust banking use.

Bank-ready checklist

Before using this topic in production, ask whether the model purpose is clear, the legal classification is understood, the population is defined, the data is governed, the validation is independent, the customer impact is tested and the approval boundary is documented.

Then ask whether the monitoring thresholds, override process, adverse action reason logic, issue management, change control, audit pack and retirement criteria are in place. Banking AI governance is only strong when the bank knows what happens after approval, not just before approval.

If the answers are strong, the model can support banking work with discipline. If the answers are weak, the model may still produce a result, but the bank should not treat that result as controlled evidence for customer, regulatory or financial impact.

Source anchors for accurate study

Federal Reserve SR 26-2, dated 17 April 2026, supersedes SR 11-7 and SR 21-8 and attaches revised model-risk guidance for banking organisations.

The 2026 interagency model-risk guidance focuses on model development and use, validation and monitoring, governance and controls, and vendor or third-party model products.

The revised model-risk guidance says model risk depends on inherent risk, exposure, purpose and use, and that practices should be tailored to the bank's risk profile and model usage.

The EU AI Act treats AI systems used to evaluate the creditworthiness of natural persons or establish a credit score as high-risk, except where the system is used for financial fraud detection.

The EU AI Act high-risk framework includes controls around risk management, data governance, technical documentation, record keeping, transparency to deployers, human oversight, accuracy, robustness and cybersecurity.

ECOA and Regulation B require creditors to provide specific and accurate reasons for adverse action; using a complex algorithm does not remove that obligation.

U.S. Regulation B, 12 CFR 1002.9, requires specific principal reasons for adverse action in covered credit decisions, including when a creditor uses an AI model. CFPB Circular 2022-03 was withdrawn on 12 May 2025; do not cite it as current guidance. Primary sources: https://www.consumerfinance.gov/rules-policy/regulations/1002/9 and https://www.consumerfinance.gov/compliance/guidance/withdrawn-guidance/.

Federal Reserve public remarks on AI in the financial system emphasise that AI is not exempt from existing laws and risk-management expectations, including fair lending, privacy, cybersecurity, third-party risk and model risk.

NIST AI RMF describes trustworthy AI through characteristics including valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.

Banking practice note: legal classification

For why shap explainability does not prove fairness, legal classification is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from SHAP values through explanation-method validation and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

AI adoption should strengthen governance here. It should make weak evidence easier to see, proxy risk easier to challenge, documentation gaps easier to find and unstable outcomes easier to monitor. It should not become a way to hide uncertainty behind dashboards, explanations or committee slides.

A good implementation records model owner, model version, source data, feature list, intended use, population, limitation, validation result, approval condition, user action, override decision, monitoring result and issue history. That record is what makes the topic useful to risk, compliance, technology, audit and business owners.

Banking practice note: model purpose

For why shap explainability does not prove fairness, model purpose is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from feature contribution through reason-code mapping and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: customer population

For why shap explainability does not prove fairness, customer population is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from local explanation through fairness testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: source authority

For why shap explainability does not prove fairness, source authority is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from global explanation through proxy-variable review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: training data

For why shap explainability does not prove fairness, training data is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from reason code through stability testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: feature governance

For why shap explainability does not prove fairness, feature governance is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from protected-group outcome through baseline sensitivity review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: protected-class testing

For why shap explainability does not prove fairness, protected-class testing is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from proxy variable through human interpretability check and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: proxy-variable review

For why shap explainability does not prove fairness, proxy-variable review is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from training sample through customer-language review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: adverse action reasons

For why shap explainability does not prove fairness, adverse action reasons is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from baseline population through legal review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: reason-code mapping

For why shap explainability does not prove fairness, reason-code mapping is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from model version through model limitation disclosure and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: explainability limits

For why shap explainability does not prove fairness, explainability limits is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from explanation method through monitoring and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: independent validation

For why shap explainability does not prove fairness, independent validation is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from validation test through override feedback and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: effective challenge

For why shap explainability does not prove fairness, effective challenge is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from customer notice through explanation-method validation and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: approval committee

For why shap explainability does not prove fairness, approval committee is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from manual reviewer note through reason-code mapping and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: documentation quality

For why shap explainability does not prove fairness, documentation quality is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from SHAP values through fairness testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: implementation evidence

For why shap explainability does not prove fairness, implementation evidence is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from feature contribution through proxy-variable review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: monitoring threshold

For why shap explainability does not prove fairness, monitoring threshold is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from local explanation through stability testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: override review

For why shap explainability does not prove fairness, override review is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from global explanation through baseline sensitivity review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: complaint feedback

For why shap explainability does not prove fairness, complaint feedback is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from reason code through human interpretability check and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: customer harm

For why shap explainability does not prove fairness, customer harm is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from protected-group outcome through customer-language review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: regulatory evidence

For why shap explainability does not prove fairness, regulatory evidence is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from proxy variable through legal review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: third-party dependency

For why shap explainability does not prove fairness, third-party dependency is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from training sample through model limitation disclosure and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: change control

For why shap explainability does not prove fairness, change control is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from baseline population through monitoring and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: incident response

For why shap explainability does not prove fairness, incident response is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from model version through override feedback and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: retirement criteria

For why shap explainability does not prove fairness, retirement criteria is not optional detail. It decides whether the bank can explain the model, defend the use and protect the customer. A model may be fast and accurate in a narrow test, but banking approval depends on whether the result is suitable for explainability governance, reason-code review, fair lending testing, validation challenge, model limitation management and customer communication control.

The practical test is to trace one item from explanation method through explanation-method validation and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: legal classification

The practical test is to trace one item from validation test through reason-code mapping and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: model purpose

The practical test is to trace one item from customer notice through fairness testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: customer population

The practical test is to trace one item from manual reviewer note through proxy-variable review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: source authority

The practical test is to trace one item from SHAP values through stability testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: training data

The practical test is to trace one item from feature contribution through baseline sensitivity review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: feature governance

The practical test is to trace one item from local explanation through human interpretability check and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: protected-class testing

The practical test is to trace one item from global explanation through customer-language review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: proxy-variable review

The practical test is to trace one item from reason code through legal review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: adverse action reasons

The practical test is to trace one item from protected-group outcome through model limitation disclosure and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: reason-code mapping

The practical test is to trace one item from proxy variable through monitoring and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: explainability limits

The practical test is to trace one item from training sample through override feedback and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: independent validation

The practical test is to trace one item from baseline population through explanation-method validation and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: effective challenge

The practical test is to trace one item from model version through reason-code mapping and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: approval committee

The practical test is to trace one item from explanation method through fairness testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: documentation quality

The practical test is to trace one item from validation test through proxy-variable review and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Banking practice note: implementation evidence

The practical test is to trace one item from customer notice through stability testing and into the business use. If the team cannot show that path without guesswork, the model pack is not mature enough for serious banking reliance.

Attribution is a different question

A SHAP value estimates how a feature contributed to a model prediction under a chosen background and method. It does not show that the training population was representative, labels were unbiased or decisions meet the bank's fairness obligations. A model may assign large weight to a seemingly neutral postcode-related feature that reflects historic differences in access to credit. A tidy attribution chart cannot settle that question.

For two declined applicants, check source feature validity, score and policy threshold, then compare outcome patterns across relevant populations. Investigate proxies and missingness, and test whether explanations remain stable under reasonable reference samples. Review reason statements for accuracy and customer usefulness. Fairness assessment also needs approval and referral rates, error and calibration where outcomes mature, and the consequences of overrides. Explanation is useful evidence, not a substitute for these tests.