Description

PSPC Human Capital Management aims to address and eliminate the existing backlog of pay cases by implementing an AI assistant designed to support compensation agents (CA). The primary objective of the AI assistant is to offer advice and necessary information to CAs and support them in expediating case closure. Currently, the tool is applied to synthetic data on select case types (actings) that are over 365 days old. Adopting a hybrid-by-design approach, this initiative aims to harness the strengths of both human insight and machine efficiency. This approach enables rapid data consumption and recommendations by machines, while CAs retain decision-making authority over how the data is used, ensuring a balanced and effective resolution process. The project will focus on aggregating relevant case data and information, assessing it for accuracy and completeness, and validating it against authoritative sources such as approved standard operating procedures (SoPs), job aids, directives, and policy documents. This comprehensive strategy aims to enhance the efficiency and accuracy of CAs work, facilitate faster case resolutions, and elevate the quality of service. Treasury Board’s [Directive on Automated Decision-Making](https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592) mandates the use of the Algorithmic Impact Assessment (AIA) tool to evaluate the risks and impacts of automating any decision-making process. Additional details on the structure and use of the AIA, including how to interpret the scoring and impact levels assigned by the tool, can be found at the [Algorithmic Impact Assessment tool - Canada.ca](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html) website. ___________________________________ Details of AIA releases: April 2024, Version 1: testing with synthetic data began August 2024, Version 2: testing with production data began

Resources

Name	Format	Description	Link
	33		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v1-fra.pdf
	23		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v1.json
	33		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v1-eng.pdf
	21		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v1-fra.html
	23		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v2-2.json
	21		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v2-2-eng.html
	33		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v2-2-eng.pdf
	33		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v2-2-fra.pdf
	23		https://open.canada.ca/data/dataset/061ee9d1-a04a-4590-a381-df8527390f68/resource/017a2538-15b2-4776-b505-b23def9307db/download/aia-results-august-update-v2.2-en-and-fr.json
	21		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v2-2-fra.html
	21		https://donnees-data.tpsgc-pwgsc.gc.ca/bh1/eiaia-aiaai/eia-ia_aia-ai-v1-eng.html

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