AI System Advisory & Development • AA-04
Explainability & Human Oversight
Make automated decisions explainable to the person they affect.
Scope
What the engagement covers.
Where AI influences decisions about people, explanation and oversight are obligations rather than features. This service designs both, and the evidence that they operate.
Included capabilities
- Explainability requirements by decision type and affected-person impact
- XAI implementation appropriate to model class and audience
- Human oversight design: meaningful review, not rubber-stamping
- Automated decision governance including contest and appeal routes
- Transparency artefacts: notices, model cards and decision records
Outputs and deliverables
- Explainability requirements by decision type
- XAI implementation and evaluation report
- Human oversight workflow and reviewer guidance
- Model cards, notices and decision record templates
Workflow
How it is delivered, step by step.
Each step has an owner, an entry condition and an artefact that has to exist before the next step begins.
01ScopeWhich decisions require explanation, to whom, and at what depth.
02DesignExplanation method per model class and per audience.
03ImplementTechnical explainability plus the human review workflow.
04TestWhether reviewers can and do meaningfully change outcomes.
05EvidenceDecision records, notices and appeal handling documented.
Use cases
Where this is typically applied.
Public-sector automated decisions affecting citizen entitlements
Any system where affected people have a right to contest outcomes
Delivery model
The operating pattern for AI System Advisory & Development.
The same delivery discipline applies across every capability in this line, so combined engagements stay coherent.
Discover
Inventory use cases, models, data, vendors, users and regulatory context.
Classify
Assess materiality by impact, autonomy, data sensitivity and deployment context.
Design
Governance, roles, policy, controls, lifecycle gates and evidence requirements.
Test
Risk assessment, architecture review, red teaming and scenario exercises.
Implement
Controls in delivery workflows, platforms, models and human oversight.
Sustain
Reporting, incidents, change, re-assessment, training and improvement.
Integration
- Model registry, feature store and MLOps pipelines for lifecycle gates.
- GRC platform for AI control mapping, evidence and issue management.
- Data catalogue and lineage tooling for dataset provenance.
- Security stack for logging, monitoring and incident handling of AI systems.
Engagement approach
Baseline engagements are a readiness and risk assessment. Build engagements operationalize the framework. Assurance engagements test models, LLM applications, agents and the governance controls around them, then validate remediation.
Related
Other capabilities in AI System Advisory & Development.
AA-01
AI Strategy & Readiness
Decide where AI is worth doing before deciding how.
AA-02AI Risk, Compliance & Assurance
Identify, evaluate, treat and evidence AI risk in a form auditors accept.
AA-03Secure AI Lifecycle Development
Build the controls into the pipeline rather than reviewing at the end.
AA-05AI Capability Development
Give decision-makers and builders the skills the strategy assumes they have.
AA-06AI Solution Implementation
Take responsible AI requirements all the way into working systems.