Risk Scorers
Risk scorers define how a metric value is converted into a risk score for your AI app. Each risk scorer specifies an expression (function) that computes the risk from the metric value and optional parameters.
Risk Scorer Overview
Properties
key string required
Unique identifier assigned to the entity in AI GO!.
Pattern: ^[a-zA-Z0-9_\-]+$
Max Length: 250
display_name string required
The risk scorer’s name displayed to the user.
description string
Short description of the risk scorer.
Default: None
function string required
An function expression written in Python-like math syntax (e.g. impact * (1 - metric_key)) where every variable must be covered by config_spec or metric_key.
metric_key string required
The variable in function that will receive the metric value.
config_spec array[FloatParameterSpec, IntParameterSpec, CategoricalParameterSpec]
Parameter specifications that configure this risk scorer.
Default: []
Performance Risk Scorer
key: "performance_risk_scorer"
display_name: "Performance Risk Scorer"
description: >
Converts a model accuracy metric into a risk score by treating low accuracy as
failure and scaling it by the business impact and deployment reach of the system.
function: "(1 - accuracy) * impact * deployment_reach"
metric_key: "accuracy"
config_spec:
- type: float
key: "impact"
display_name: "Impact"
description: >
How severely poor performance in this area affects the system. Higher values
amplify the risk score for the same accuracy drop.
default_value: 5.0
min: 0.0
max: 10.0
- type: categorical
key: "deployment_reach"
display_name: "Deployment Reach"
description: >
Whether the system is externally user-facing or internal only. External
deployments amplify the score; internal deployments reduce it.
allowed_values:
- "internal"
- "external"
default_value: "external"
values_mapping:
internal: 0.5
external: 1.0