GRA · AI and Financial Model Risk
Spatial financeWhere capital meets hazard, exposure and transition
Explainability and fairness
Customers, staff and supervisors need to understand why a model made a decision, and decisions must not discriminate unlawfully. Explainability methods give reasons for individual outputs and overall behaviour; fairness testing compares outcomes across groups. Both have limits, and law sets duties such as giving reasons for credit denial. This page sets out how explainability and fairness are assessed, the evidence used and who decides.
Explainability and fairness on the record.
Explainability and fairness: how the risk develops.
Interpretation, bias and accountability. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Post-hoc explanations approximate
Methods such as feature attributions explain a complex model approximately. Different methods can give different reasons for the same decision, and none guarantees the true cause.
Fairness has several definitions
Equal approval rates, equal error rates and calibration across groups cannot all hold at once when base rates differ. Choosing a criterion is a policy and legal decision, not a technical default.
Reasons must be specific
Credit laws require specific reasons for adverse decisions. Generic reasons or ones unrelated to the actual driver do not meet the duty, whatever the model's complexity.
Opacity hides harm
Without explanation and outcome testing, unfair patterns surface only through complaints, litigation or enforcement, after many customers are affected.
Model risk lifecycle.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Model risk lifecycle
Explainability and fairness · Model inventory · Committee 3
Lifecycle
- Inventoryregistered
- Developdocumented
- Validateindependent
- Approvecommittee
- Implementcontrols
- Monitorthresholds
- Reviewannual
- Retiredecommission
Models
| MD-169 PD retail | Tier 1 | 19 months | 4 | Overdue |
| MD-140 LLM complaints triage | Tier 1 | never | 0 | Not validated |
| MD-400 IFRS 9 staging | Tier 1 | 9 months | 1 | Approved |
| MD-386 marketing propensity | Tier 3 | 17 months | 1 | Approved |
Validation scope
- Conceptual soundness
- In progress
- Outcomes analysis
- Pending
- Benchmarking
- Pending
- Use limits
- human review required
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Adverse impact ratio | ratio of favourable outcome rates between groups | periodic | Fairness testing | ESTIMATED |
| Error rate differences across groups | difference in false positive or negative rates | periodic | Fairness testing | ESTIMATED |
| Explanation stability | agreement between explanation methods | per validation | Validation testing | ESTIMATED |
| Adverse action notices citing model reasons | share of declines with specific reasons | periodic | Compliance review | DECLARED |
| Complaints alleging unfair automated decisions | count | quarterly | Complaints data | REPORTED |
| Enforcement actions on algorithmic discrimination | count | annual | Regulator records | Published statistics |
What explainability and fairness means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in explainability and fairness reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| CFPB guidance on adverse action notices and AI | United States duty to give specific reasons | Public domain |
| MAS FEAT principles and Veritas methodology documents | Fairness assessment methods for financial services | Free, MAS terms |
| IAIS Application Paper on the supervision of AI | Fairness, transparency and redress expectations for insurers | Free, IAIS terms |
| EIOPA report on governance principles for ethical and trustworthy AI | Insurance sector AI fairness and governance | Free, EU reuse terms |
| Academic literature on fairness definitions and impossibility results | Formal analysis of fairness criteria | Free to read, terms per publisher |
Who decides, and what the evidence serves.
AI and Financial Model Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Boards and conduct committees | Which fairness criteria apply to each use | Legal requirements and outcome testing |
| Model developers and validators | Which explanation methods are reliable for a model | Stability and fidelity testing |
| Conduct and equality regulators | Whether outcomes are lawful | Outcome data, complaints and testing evidence |
| Compliance functions | Whether customer explanations meet legal duties | Notice reviews and model reason codes |
Outputs this topic produces
- Guide to fairness criteria and their trade-offs
- Method note on testing explanation methods for stability
- Brief on adverse action reasons for complex models
- Questions for boards on fairness criterion choice
What experts ask.
The questions concern understanding, justice and responsibility.
- Can developers, validators, users and customers understand why a model made a decision?
- Which fairness definition is appropriate for the decision, and who chose it?
- Where does bias enter: data, design, deployment or feedback?
- Can a customer challenge an automated decision and obtain redress?
- Who is accountable when an unexplainable model causes harm?
Mechanisms and drivers.
Simple models such as scorecards are directly interpretable. Complex models, such as gradient-boosted trees and neural networks, require post-hoc explanation methods that approximate their behaviour; these approximations can be unstable or misleading. Explainability needs differ by audience: a validator needs technical depth, a customer needs a clear reason.
Bias arises from historical data, unrepresentative samples, proxy variables and feedback loops in which model decisions shape future data. Fairness has several mathematical definitions, such as equal outcomes, equal error rates or equal calibration, which generally cannot all be satisfied at once. Choosing among them is a value judgement that should be made explicitly and accountably.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Interpretable model design | Inherent transparency | May sacrifice some predictive performance |
| Post-hoc explanation methods | Feature contributions to outputs | Approximate and sometimes unstable |
| Fairness testing across groups | Disparities in outcomes and errors | Requires group data that may be unavailable |
| Impact assessment | Consequences for affected people | Relies on foresight and engagement |
How it is studied, and what the methods cannot say.
- 01AI evaluation and assurance engineeringValidation of machine learning models · Red-teaming generative AI · Fairness assessmentApplied here
- 02Artificial intelligence and machine learningGradient-boosted credit models · Language models in operations · Anomaly detectionApplied here
- 03Probability and statisticsCalibration testing · Drift statistics · Group outcome comparisonApplied here
Questions and answers.
Can a model satisfy every fairness definition?
Are feature attributions true explanations?
Does using AI change the duty to explain credit decisions?
Is a more accurate model always better?
Can fairness be fully automated?
Who decides which fairness definition applies?
Does the platform certify models as fair?
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