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Episode 25: Lisa Middlemiss on Canada’s ambitious immigration target

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Content provided by Canadian Bar Association. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Canadian Bar Association or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In 2022, Canada experienced a major milestone as it welcomed over 437,000 immigrants, leading to annual population growth of over one million for the first time in history. The country has set an even more ambitious target of welcoming 500,000 new immigrants yearly by 2025. While there are compelling arguments in favor of accelerating immigration, such as addressing labor shortages and countering an aging population, implementing the plan is no easy feat.

One significant hurdle lies in the fact that Quebec sets its own immigration targets, which happen to be significantly lower than those set by the federal government in Ottawa.

Moreover, the episode explores the emerging questions surrounding the use of artificial intelligence (AI) in processing the vast number of immigration applicants. As the applicant pool expands, the effectiveness and fairness of AI systems come under scrutiny, raising important ethical considerations.

We also examine the challenges involved in the management of refugees and asylum claims.

Lisa Middlemiss is a lawyer with Gomberg Dalfen in Montreal. She works on immigration-related matters with an emphasis on temporary residence, Labour Market Impact Assessments and permanent residence issues, such as spousal sponsorships, She also advises on the Quebec Skilled Worker and Express Entry programs.

She is also the National Chair of CBA Immigration Law Section, and will open the upcoming CBA Immigration Law Conference, to be held in Ottawa from June1 to 3rd.

To contact us (please include in the subject line "Podcast"): national@cba.org

  continue reading

37 episodes

Artwork
iconShare
 
Manage episode 363670335 series 2985293
Content provided by Canadian Bar Association. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Canadian Bar Association or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In 2022, Canada experienced a major milestone as it welcomed over 437,000 immigrants, leading to annual population growth of over one million for the first time in history. The country has set an even more ambitious target of welcoming 500,000 new immigrants yearly by 2025. While there are compelling arguments in favor of accelerating immigration, such as addressing labor shortages and countering an aging population, implementing the plan is no easy feat.

One significant hurdle lies in the fact that Quebec sets its own immigration targets, which happen to be significantly lower than those set by the federal government in Ottawa.

Moreover, the episode explores the emerging questions surrounding the use of artificial intelligence (AI) in processing the vast number of immigration applicants. As the applicant pool expands, the effectiveness and fairness of AI systems come under scrutiny, raising important ethical considerations.

We also examine the challenges involved in the management of refugees and asylum claims.

Lisa Middlemiss is a lawyer with Gomberg Dalfen in Montreal. She works on immigration-related matters with an emphasis on temporary residence, Labour Market Impact Assessments and permanent residence issues, such as spousal sponsorships, She also advises on the Quebec Skilled Worker and Express Entry programs.

She is also the National Chair of CBA Immigration Law Section, and will open the upcoming CBA Immigration Law Conference, to be held in Ottawa from June1 to 3rd.

To contact us (please include in the subject line "Podcast"): national@cba.org

  continue reading

37 episodes

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