Algorithmic Impact Assessment - Machine Learning Workload_FR (French, PDF)

View metadata and access the original French PDF file: Algorithmic Impact Assessment - Machine Learning Workload_FR.

Format

PDF

Access

file-only

Size

221,677 bytes

Publisher-reported update

2024-04-12

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…

Access original file