A loan model performs worse for one age group. Which tool helps investigate?
Why D is the answer
The Responsible AI dashboard
It brings together error analysis, fairness assessment and interpretability.
In detail
- The Responsible AI dashboard analyzes errors and fairness across groups.
- Use it before release as well.
Use case
An illustrative scenario, not a real incident.
A loan model does worse for one age group. The Responsible AI dashboard shows where errors cluster, and why.
See it in different ways
flowchart LR A["Error analysis"] --> B["Fairness assessment"] B --> C["Interpretability"] C --> D["Counterfactuals"]
stateDiagram-v2 [*] --> Trained Trained --> Registered: version and lineage Registered --> Deployed Deployed --> Monitored Monitored --> Retrained: drift alert Retrained --> Registered Deployed --> Archived: replaced
mindmap
root(("Responsible AI dashboard"))
n1["Errors"]
n2["Where they cluster"]
n3["Fairness"]
n4["Across groups"]
n5["Explanations"]
n6["Which features matter"]Key point: The dashboard investigates errors and fairness.
Why the other options are wrong
- A. A Bicep template for the workspace
- Bicep defines infrastructure.
- B. A GitHub Actions cache
- A cache speeds up workflows.
- C. A managed virtual network rule
- A network rule controls traffic.