Algorithmic Impact Assessment - Employment Insurance Machine Learning Workload

Algorithmic Impact Assessment - Employment Insurance Machine Learning Workload ## EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation Processes Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits. During the recalculation process, the program implements specific measures based on the outcomes: - In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall. - Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount. - When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged. The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change. This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement. This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM). These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards. In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted. The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years. This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks. A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated. 2024-04-26 Employment and Social Development Canada open-ouvert@tbs-sct.gc.ca Government and PoliticsEIEmployment InsuranceMachine Learningrandom forest Algorithmic Impact Assessment - Machine Learning Workload_ENPDF https://open.canada.ca/data/dataset/6b429c8e-ee5b-451a-883f-b6180ada9286/resource/2694367e-babf-4d87-a852-827e4178141d/download/1-aia_ei_ml_workload_en.pdf Algorithmic Impact Assessment - Machine Learning Workload_FRPDF https://open.canada.ca/data/dataset/6b429c8e-ee5b-451a-883f-b6180ada9286/resource/4491b7b1-6b15-4398-87a3-59a3d34aef0a/download/1-aia_ei_ml_workload_fr.pdf Algorithmic Impact Assessment - ResultsJSON https://open.canada.ca/data/dataset/6b429c8e-ee5b-451a-883f-b6180ada9286/resource/19182fb2-ae7a-4775-a7c7-7b8c010d84b9/download/aia-results_json.json
EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation Processes

Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits.

During the recalculation process, the program implements specific measures based on the outcomes:

  • In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall.
  • Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount.
  • When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged.

The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change.

This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement.

This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM).

These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards.

In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted.

The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years.

This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks.

A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated.

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