*** Amendment # 003 - The closing date and time of this RFP is hereby extended to:
September 14th, 2018 at 15:00 (3:00 pm) Eastern Daylight Saving Time (EDT)
-----------------------
The Canada School of Public Service (CSPS) is engaging organizations that employ artificial intelligence methods, including advanced data analytics (ADA) and machine learning (ML) approaches, to offer new insight and analysis on federal regulations. This information may assist in identifying opportunities for regulatory modernization in order to ensure that regulations support innovation, growth and competitiveness while protecting the health, safety, and well-being of Canadians, and the environment.
To this end, the purpose of this work is to use ADA and ML methods to examine the large and diverse stock of over 2,600 federal regulations. This work could, for example, contribute to the following themes:
carrying out a linguistic analysis of regulations,
providing insight on consolidation/aggregation/streamlining of regulations,
identifying areas of compliance/reporting burden, and,
providing other regulatory insights based on own knowledge or review of issues identified by stakeholders.
The resulting analysis products would be publicly available, and inform advice provided by the Treasury Board of Canada Secretariat leading up to the federal Budget 2019.
One information session for vendors is planned for August 14th, 2018 and will take place in the Ottawa-Gatineau area with a WebEx option.
The detailed information including the WebEx session information will be sent by email to interested parties. To receive the information, please provide your full name, email address to the Contracting Authority listed in this RFP, and ensure that the RFP # is included (24062-19-096) in the subject line of the email. Please also indicate areas of concern/inquiry, for example, procurement, vendor list, deliverables, subject matter, etc.) and any specific questions if possible. Interested parties must email the Contracting Authority or their replacement prior to the session.