Below are remarks made by Dr. Fenwick McKelvey to the Standing Committee on Human Resources, Skills and Social Development and the Status of Persons with Disabilities
Introduction
I am an Associate Professor in Information and Communication Technology Policy at Concordia University. My research addresses the intersection of algorithms and AI in relation to technology policy.
I submit these comments in my professional capacity representing my views alone.
Background
I am speaking from the unceded Indigenous lands of Tiohtià:ke/Montréal. The Kanien’kehá:ka Nation is recognized as the custodians of the lands and waters on which we gather today.
I want to begin my connecting this study to the broader legislative agenda then provide some specific comments about the connection between foundational models trained off public data or other larger data sets, and the growing concentration in the AI industry.
Canada is presently undergoing major changes to its federal data and privacy law, C-27, that grants greater exemptions for data collection. These exemptions enable greater use of machine learning and other data-dependent classes of AI technologies, putting tremendous pressure on a late amendment, the Artificial Intelligence and Data Act, to mitigate high-risk applications and plausible harms. Labour, automation, workers’ privacy and data rights should be important considerations for this bill as seen in the US AI Executive Order. I would encourage this committee to study the effects of C-27 on workplace privacy and consequences of a more permissive data environment.
As for the relationship between labour and artificial intelligence, I wish to make three major observations based on my review of the literature and a few recommendations.
- AI will affect the labour force and these effects will be unevenly distributed.
- AI’s effects are not simply about automation, but about the quality of the work.
- The current global arrangement of AI is concentrating power in a few technology firms.
AI will affect the labourforce and these effects seem to be unevenly distributed.
I grew up in Saint John, New Brunswick under the shadow of global supply chains and a changing workforce. My friends all worked in call centers. Now these same jobs will be automated by chatbots or at least assisted through generative AI. My own research has shown that a driving theme in discussing AI in the telecommunication services focuses on automating customer contact.
I begin with call centers because as we know from the work of Dr. Edna Brophy that “workforce…is female, precarious, and mobile”. The examples serve as an important reminder that AI’s effects may further marginalize workers targeted for automation.
AI’s effects seem to already be affecting precarious outsourced workers according to reporting from the Rest of the World. Understanding the intersectional effects of AI is critical to understanding its impact on the labour force. We are only beginning to know how Canada will fit into the global shifts, how Canada might export more precarious jobs abroad as well as find job growth in new sectors or regions.
Finally, workers are increasingly finding themselves subjected to algorithmic management. Combined with a growing turn toward workplace surveillance as being studied by Dr. Adam Molnar, there is an urgent need to understand and protect workers from invasive data gathering that might reduce their workplace autonomy or even train their less-skilled or automated replacements. According to the OECD, workers subjected to algorithmic management have a larger reported feeling of a loss of autonomy.
Quality of work
All the promises of AI hinge on being able to do work more efficiently. But who benefits from this efficiency? OECD studies have found that “AI may also lead to a higher pace and intensity of work.” The impacts seem obvious and well established by past studies of technologies like the Blackberry that shifted workplace expectations and encouraged an always-on expectation of workers.
These emphasize the need to consider AI effects not just on jobs, but the quality of work itself.
Concentration of power
The introduction of generative AI marks has arrived embedded into key productivity suites, named Microsoft Office, Google Docs, and Adobe Creative Cloud. My final comment, which is less about AI than about its particular configuration now, is that we are seeing a growing reliance on a few technology platforms that have become critical infrastructure for workplace productivity. AI might lock in these firms’ market power as their access to data and cloud computing power makes it difficult to compete. Past examples demonstrate that communication technology favours monopolies without open standards and efforts to decentralized power.
I am happy to discuss remedies and solutions in the question and answer period, but I encourage the committee to:
- Investigate better protection of workers and workers’ rights including greater data protection and safeguards and enforcement of workplace surveillance especially to ensure works can’t train themselves out of a job;
- Consider arbitration and greater support in bargaining power especially for contracts between independent contractors and large technology firms arrangements;
- Ensure efficiency boosts are fairly distributed, such as considering a 4-day work week, raising minimum wages, and A right to disconnect from work.
Thank you for your time and this opportunity.
