Algorithmic Impact Assessment - Employment Insurance Machine Learning Workload
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Additional Info
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Last Updated | October 22, 2024, 16:01 (UTC) |
Created | October 1, 2024, 07:41 (UTC) |
Domain / Topic
Domain or topic of the dataset being cataloged.
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Description
A description of the dataset.
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EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation ProcessesRecalculation 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:
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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Format (CSV, XLS, TXT, PDF, etc)
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Dataset Size
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Metadata Identifier
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Published Date
Published date of the dataset.
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2024-04-15 |
Time Period Data Span (start date)
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Time Period Data Span (end date)
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GeoSpatial Area Data Span
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Access category
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License
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Open Government Licence - Canada |
Limits on use
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Location
Location of the dataset.
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Data Service
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Owner
Owner of the dataset.
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Employment and Social Development Canada | Emploi et Développement social Canada |
Contact Point
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Contact Point Email
The email to contact regarding access?
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Publisher
Publisher of the dataset.
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Publisher Email
Email of the publisher.
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open-ouvert@tbs-sct.gc.ca |
Accessed At
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Identifier
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Language
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Link to dataset description
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Persistent Identifier
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Globally Unique Identifier
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Contains data about individuals
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Contains data about identifiable individuals
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Source
Source of the dataset.
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https://open.canada.ca/data/en/dataset/6b429c8e-ee5b-451a-883f-b6180ada9286 |
Version notes
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Is version of another dataset
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Other versions
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Provenance Text
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Provenance URL
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Temporal resolution
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GeoSpatial resolution in meters
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GeoSpatial resolution (in regions)
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
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Number of data rows
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Number of entities
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Number of data properties
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Data quality
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Metric for data quality
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