
Publication
Canada: Agribusinesses tackle new challenges in employing foreign workers
Agribusinesses traditionally rely on temporary foreign workers (TFWs) to fulfill operational needs. COVID-19 poses new challenges for these businesses.
Global | Publication | October 2020
The COVID-19 pandemic has had an unprecedented effect on society and the global economy. It has created a new awakening among consumers about the importance of food security, food safety and ensuring proper commodity and food supply chains are in place which utilize safe, locally-sourced commodities and foods. In this section, we explore some of the legal and regulatory changes and challenges that are affecting food companies and agribusinesses all along the value chain from foreign worker mobility and accommodation restrictions, constrains on the supply of certain foreign-sourced products globally, trade disruptions, new protectionist and anti-trust policy developments, and important clarifications with respect to employee health and safety standards.
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Agribusinesses traditionally rely on temporary foreign workers (TFWs) to fulfill operational needs. COVID-19 poses new challenges for these businesses.
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As Canadians continue to self-isolate and eat more meals at home, demands on Canada’s grocery stores and food manufacturers have dramatically increased.
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Following on the heels of new federal support programs for producers employing temporary foreign workers (TFWs), several provinces have announced initiatives to assist agribusinesses faced with an expected shortage in farm workers.
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In Part 1 of this series on fair use in training large language models (LLMs), we discussed Judge Alsup’s decision of Bartz v Anthropic, which found that copying books to train an LLM was fair use, but using pirated books to create a central library was not.
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According to a joint report by the Office of the Superintendent of Financial Institutions and the Financial Consumer Agency of Canada, some 70% of federal financial institutions expect to be using AI by 2026.
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Two recent judgments – decided just days apart – from different judges of the Northern District of California District Court determined that using copyrighted books to train large language models (LLMs) was fair use.
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