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Configuring model transparency policy

Updated on July 5, 2022

By configuring the transparency threshold for a business issue and optionally adapting the transparency score for models, lead data scientists determine which model types are compliant for that issue.

Before you begin: To change model transparency policies you need pzModelTransparency privilege. Check with your system administrator if you have this privilege. For more information, see Granting privileges to an access role.
Non-compliant models might be forbidden to use by certain company policies. Each model type has a transparency score ranging from 1 to 5, where 1 means that the model is opaque, and 5 means that the model is transparent. Highly transparent models are easy to explain, whereas opaque models might be more powerful but difficult or not possible to explain. Depending on the company policy, models are marked as compliant or non-compliant. Model compliance is also included in the model reports that you can generate in the Prediction Studio.
  1. In the navigation pane of Prediction Studio, click SettingsModel transparency policies.
  2. In the Transparency thresholds section, set the transparency threshold for each business issue.

    The transparency threshold can be different for each business issue. For example, the Risk issue can have a higher threshold than the Sales issue. It means that models that are used for predicting risk need to be easy to explain.

  3. Optional: In the Model transparency scores section, change the transparency score for individual model types.
  4. Click Save.
    Model transparency policies
    Transparency thresholds and transparency scores for each model type

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