The Partnership on AI: A Multi-Stakeholder Coalition for Responsible Technology

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By Kiera Schuller

Katya Klinova is Program Lead at the Partnership on AI (PAI), a coalition of over 100 organizations from civil society, industry, and academia, where her work focuses on AI, Labour, and the Economy. Prior to joining PAI, Katya was at the Harvard Kennedy School of Government, researching the potential impact of AI advancement on economic growth trajectories of developing countries. Previously, she worked at the United Nations Executive Office of the Secretary-General (SG) to prepare the launch of the SG’s Strategy for New Technology, and at Google in a variety of roles.

Kiera:

AI, as it advances, will influence the nature of work in Canada and globally. AI advances can inject great value into the economy, but they can also cause disruptions as new kinds of work are created and others become less needed. Can you highlight the most pressing disruptions and/or issues relating to AI and labour at the moment in Canada, the US, and/or globally?

Katya:

I group the impact uncertainties into two buckets of questions: one question around labour demand and another question around quality of jobs. On the former, the question is whether the labour demand is going to go down for certain groups, and for whom? From historical experience, technology usually automates some tasks but also creates new tasks, but the question is: do these processes balance out?
Research by Acemoglu and Restrepo for the US shows that in the decades following World War II, automation and task-creation balanced out nicely. But that balance has tipped toward automation that has been accelerating, while the creation of new tasks has slowed down in the past three to four decades. But we also need to ask: “Who are these tasks for? What tasks are we automating, and whom are we taking them away from?” If we are automating tasks that don’t require college degrees and only creating tasks that require select college or graduate degrees, then we are creating a skills bias. This is the kind of technological change that advantages the highly skilled and those with a lot of educational attainment while disadvantaging people who did not have the resources to acquire that education. Here then, educational efforts become even more important, and we have to be realistic about how quickly we can ramp those educational changes up. For whom is upskilling or retraining available? How flexible is the labour market? If we make changes faster than people can adapt to them, or if people do not have the resources to adapt, it will be a difficult transition for entire groups within society, independent of whether they are in developing or developed countries.
The second bucket of questions is around the quality of jobs. For example, quality in data-labelling jobs is an issue as companies rely more and more on contingent, temporary workforces. It’s beneficial for them to bring in a workforce only when they need them, but at the same time, our societal structures are not set up to support workers involved in those kinds of work. Where worker benefits, healthcare, and pensions are tied to an employer, it is very important to be a full-time employee, so the question of portable benefits and other support structures for crowd platform workers is very important and needs to be addressed.

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