Welcome
User Guide
Trusted AI
This feature is available for classification and regression problem types.
Fairness Metrics
Fairness metrics are a set of measures that enables the user to detect the presence of bias in model.
Viewing Fairness Metrics
To view the fairness metrics, complete the following steps:

Release Notes
Installation Guide
Developer Guide
MLAC User Guide
User Guide
- Introduction
  Marketing AION (Artificial Intelligence ON) is an AI life cycle management platform for applying machine learning to real-world problems. Marketing AION encompasses the complete pipeline from raw data ingestion to a deployable machine learning model with a less-code/no-code approach.
- Model Management
- Model Training
- Model Prediction
  Model prediction predicts the input data based on the pattern of training data.
- Trusted AI
  This feature is available for classification and regression problem types.
  - Fairness Metrics
    Fairness metrics are a set of measures that enables the user to detect the presence of bias in model.
    - Viewing Fairness Metrics
      To view the fairness metrics, complete the following steps:
  - Performance
    Model performance is an assessment of the model's ability to perform a task accurately not only with training data but also in real-time with runtime data when the model is deployed.
  - Transparency
    Transparency helps users to understand the input data, algorithm and expected output of an ML model.
  - Explainability
    Explainability tab gives an explanation about Prediction and Model.
  - Robustness
    Robustness of AI/ML models is analyzed by Sensitivity Analysis examining the extent to which the results are affected by changes in input data.
- Model Monitoring
  This feature is available for classification and regression problem types.
- Utility
  For Data Generator and BYOM (Bring Your Own Model), select the Utility tab.

Viewing Fairness Metrics

To view the fairness metrics, complete the following steps:

Procedure

Select
In the Predict column, select .
From the Check Bias For dropdown box, select the protected feature. When training the model, the Target feature is automatically selected.
Select the graph type.
To view the graph, click the Show Metric tab.

Note: The acceptable threshold is highlighted in green. A class is considered biased if metrics-score is outside the threshold range.