Algorithmic Impact Assessment - Electronic Social Insurance Number Automation (eSINa)
When an individual applies for a Social Insurance Number (SIN), multiple channels are available to submit their application. eSIN was implemented in April 2020 in response to the COVID-19 pandemic. For the first time, individuals were able to apply for their SIN online, eliminating the need to visit a Service Canada Centre in person or the need to mail in their application. The eSIN application result was received by mail. In August 2023, a new feature was added to the My Service Canada Account (MSCA) that allows individuals to register for an account without a SIN. Once registered, SIN information can be obtained directly in MSCA.
When a SIN application is received, documents provided are analysed for authenticity and submitted for validation against an authoritative source. If the verification is successful, the application is processed, and the SIN is issued.
The primary objective of the eSINa initiative is to automate the processing of eligible SIN applications submitted via eSIN by using Artificial Intelligence and Optical Character Recognition technologies. The SIN program will continue to innovate in these aspects for the coming years.
This approach allows eligible eSIN clients to receive their SIN in near real-time via their MSCA Account and helps address the growing demand for SINs at Service Canada Centers, thus reducing Employment and Social Development Canada’s (ESDC) dependency on paper-based processes and strengthening its integrity.
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, relevant stakeholders including the Privacy Management Division, IT Security, Legal Services, Data Governance and Enterprise Architecture have been engaged over the duration of this process.
Please note: This process of automating SIN application does not change SIN eligibility, nor does it limit who can apply for a SIN. SIN automation will not reject an application. Instead, any potential rejection will be reviewed by an agent.
Datasets available for download
Additional Info
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| Last Updated | October 20, 2025, 02:15 (UTC) |
| Created | October 20, 2025, 02:15 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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Algorithmic Impact Assessment - Electronic Social Insurance Number Automation (eSINa) |
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Description
A description of the dataset.
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When an individual applies for a Social Insurance Number (SIN), multiple channels are available to submit their application. eSIN was implemented in April 2020 in response to the COVID-19 pandemic. For the first time, individuals were able to apply for their SIN online, eliminating the need to visit a Service Canada Centre in person or the need to mail in their application. The eSIN application result was received by mail. In August 2023, a new feature was added to the My Service Canada Account (MSCA) that allows individuals to register for an account without a SIN. Once registered, SIN information can be obtained directly in MSCA. When a SIN application is received, documents provided are analysed for authenticity and submitted for validation against an authoritative source. If the verification is successful, the application is processed, and the SIN is issued. The primary objective of the eSINa initiative is to automate the processing of eligible SIN applications submitted via eSIN by using Artificial Intelligence and Optical Character Recognition technologies. The SIN program will continue to innovate in these aspects for the coming years. This approach allows eligible eSIN clients to receive their SIN in near real-time via their MSCA Account and helps address the growing demand for SINs at Service Canada Centers, thus reducing Employment and Social Development Canada’s (ESDC) dependency on paper-based processes and strengthening its integrity. 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, relevant stakeholders including the Privacy Management Division, IT Security, Legal Services, Data Governance and Enterprise Architecture have been engaged over the duration of this process. Please note: This process of automating SIN application does not change SIN eligibility, nor does it limit who can apply for a SIN. SIN automation will not reject an application. Instead, any potential rejection will be reviewed by an agent. |
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Tags / Keywords
Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2025-09-29 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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License
License used to access the dataset.
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Open Government Licence - Canada |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://open.canada.ca/data/en/dataset/94d9dd91-803a-458c-a381-db6a22c9571a |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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Employment and Social Development Canada | Emploi et Développement social Canada |
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Contact Point
Who to contact regarding access?
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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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[email protected] |
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Author
Author of the dataset.
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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Identifier
Unique identifier for the dataset.
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Language
Language(s) of the dataset
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Link to dataset description
A URL to an external document describing the dataset.
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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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
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
If tabular dataset, total number of cells with data.
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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