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Policies and Policy Trail


Post doing stress testing, users can identify multiple areas where the models fail and are often contribute very high business continuity risks. Also, each business would have definitive guidelines that they would like to impose on models too. All these can be defined as 'Policy' in AryaXAI.

Essentially, Policies are the “rules/Guidelines” you can write to override a model prediction. The framework will impose the 'policies' on the models and follow the instructions as provided by the user.

Through GUI

Policies (Main menu on the left) > Create Policy

Define the policy and define the feature (data point on which you want to write the policy on). Select the conditional operators (Viz. not equal to, equal to, greater than, less than) and current expression. 

Add the policy statement, select the input under ‘Decision’ and mention the decision value you want in the final prediction, and select ‘Save’.

All policies are displayed on the Policy dashboard. You can easily Activate/ Deactivate, edit or delete the policies from here.

When viewing cases, the ‘Policies’ tab (ML Explainability > View cases > ‘view’ under the Options column) will display the policy details for the particular case.

AryaXAi: Case-wise policy view

Here, ‘Model Prediction’ is the original model prediction and ‘Final prediction’ is the overridden prediction based on the custom rules defined.

Through SDK


Help function to create a new policy:


Additional functions:

#View policies for project

#Delete Policy

Policy Trail

Similar to the "Observations Trail" the "Policy Tail" functions in a comparable manner but specifically for policies. It could log events related to policy creation, modifications, updates, or any other relevant actions taken within the policy management system. 

This feature assists in tracking the evolution of policies, understanding the sequence of modifications, and identifying who made which changes and when. It can be valuable for compliance, auditing, troubleshooting, and ensuring transparency and accountability.

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