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The relationship between social policy and employment in the era of AI

Updated: May 6

Artificial intelligence (AI), which was first developed almost 60 years ago, has transformed from a mysterious academic discipline into a potent force for social and economic change. A wide range of commonplace technologies, such as web search, medical diagnosis, smart phone applications, and most recently driverless vehicles, are now built on AI. Natural language processing, speech recognition, and pattern identification have all been significantly enhanced by deep learning, a type of machine learning based on layered representations or neural networks.

The Oxford Martin School released a report in 2013 that According to a survey, the growth of AI technology might put 47% of US occupations at risk of automation within the next two decades. Similar issues were brought up by the Obama administration in a presidential report on AI last year. According to the White House study, In order to prevent labor disruption, "Artificial Intelligence, Automation and the Economy" stated that AI-driven automation points to the necessity for bold governmental policies and a stronger safety net. Predictions of the total amount of job losses are, of course, inherently speculative, but a recent study by According to McKinsey, over the following ten years, certain jobs will be automated.

How may the government lessen the effects of AI given the likelihood of an economic future in which significant portions of the working population face the possibility of losing their employment or having them degrade in quality? A This issue has been thoroughly examined in a recent paper from the Mowat Centre at the University of Toronto, which also outlines potential solutions for decision-makers to take into account. Governments can explore the following social policies in the near future and with relatively little difficulty by looking to their own mandates and collaborations with the commercial sector and organized labor:

  1. More flexible work arrangements for both private and public sector employers, modeled on Germany’s Kurzarbeit scheme (designed to allow companies to reduce workers’ hours during economic downturns). Scaling back hours for 50 workers whose tasks are automated is a preferable outcome to layoffs for 10 workers.

  2. Promoting private sector education and training programs that re-train workers to adapt to new, more complex and technologically advanced operating environments. AT&T in the United States, for example, offers up to $8,000 a year towards tuition for degrees and online courses to its employees. Government tax rebates or co-sponsorships of similar programs could help incentivize smaller enterprises to consider similar programs.

  3. Reassessing the role of government as a purchaser of goods and services with an eye toward driving innovation. The US federal government spends over $400B a year on contracts (two-thirds by the Defense Department) and already targets some of those funds to help small businesses. If government procurement practices can help create and sustain better quality work where merited, that should be a priority. Tying procurement expenditures to job quality expectations throughout the supply chain could benefit young people and others disproportionately impacted by the rise of precarious work.

  4. Redoubling efforts to streamline from public service delivery, in light of fiscal constraints and expected increases in demand for social supports flowing from anticipated demographic and labor market trends. With 850,000 public sector jobs in Britain potentially at risk due to automation, a key focus for governments in advanced economies must be frank conversations about managing the transition into an automated future.

Other options that merit consideration over the longer-term that may come with significant cost and implementation challenges include program and policy adjustments such as:

  1. A reform of labor market support programs to promote more flexibility for employees and employers, with a strong emphasis on practical, quick outcomes-focused training. Recognizing that more people may be out of work for longer, and bouncing between jobs more frequently, will require rethinking unemployment schemes to make them more generous and more actively relevant by incorporating training.

  2. Introducing portable benefits for independent workers so that pension and health care benefits can be taken from gig to gig while requiring contributions from technology platforms that employ these workers. It is time to modernize employment classifications for independent workers, to focus on the nature of their work.

  3. Adopting more effective approaches to capturing corporate taxes from technology companies and grey market activities in order to fund social programs such as training or housing. Coordinated efforts to combat tax evasion could be worth, conservatively, between $100 billion and $240 billion.

Progressive tax regimes that fund effective education, income support, health and housing programs will become more important in a future where many are working fewer hours for less pay. Policymakers need to acknowledge that dwindling work in the era of AI may look entirely different from the past. Deploying efforts to prepare for that future will go a long way to mitigating the potentially far-reaching impacts of automation on millions of workers around the world.

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