Thank you very much for this opportunity to speak here today.
I'm 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 today in my professional capacity, representing my views alone.
I'm speaking from the unceded indigenous lands of Tiohtià:ke or Montreal. The Kanienkehaka nation is recognized as the custodians of the lands and waters from which I join you today.
I want to begin by connecting this study to the broader legislative agenda and then providing some specific comments about the connections between foundational models trained off public data or other large datasets and the growing concentration in the AI industry.
Canada is presently undergoing major changes to its federal data and privacy law through C-27, which grants greater exemptions for data collection as classified for legitimate business purposes. 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 U.S. AI executive order. I would encourage this committee to study the effects of C-27 on workplace privacy and the 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. First, AI will affect the labour force, and these effects will be unevenly distributed. Second, AI's effects are not simply about automation but about the quality of work. Third, the current arrangement of AI is concentrating power in a few technology firms.
I grew up in St. John, New Brunswick, under the shadow of global supply chains and a changing workforce. My friends all worked in call centres. 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 telecommunication services focuses on automating customer contact.
I begin with call centres because, as we know through the work of Dr. Enda Brophy, that work is “female, precarious, and mobile.” The example serves 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 Rest of World. Understanding the intersectional effects of AI is critical to understanding its impact on workforce. We are only beginning to see how Canada will fit into these global shifts and how Canada might export more precarious jobs abroad as well as find new sources of job growth across its regions and sectors.
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 less skilled workers or automated replacements. According to the OECD, workers subjected to algorithmic management have a larger reported feeling of a loss of autonomy.
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 impact seems obvious and well established by past studies of technology like the BlackBerry, which shifted workplace expectations and encouraged an always-on expectation of the worker. Other research suggests that AI has the biggest benefits for new employees. The presumed benefit is that this enables workers to make a contribution more quickly, but the risk is that AI contributes to a devaluing or deskilling of workers. These emphasize the need to consider AI's effects not just on jobs but on the quality of work itself.
The introduction of generative AI marks a change in how important office suites like Microsoft Office, Google Docs and Adobe Creative Cloud function in the workplace. My final comment here is less about AI's particular configuration now, but instead about a growing reliance on a few technology platforms that have become critical infrastructure for workplace productivity and are rapidly integrating generative AI functions. AI might lock in these firms' market power as their access to data and cloud computing might make it difficult to compete, as well as for workers to opt out of these products and services. Past examples demonstrate that communication technology favours monopolies without open standards or efforts to decentralize power.
I am happy to discuss remedies and solutions in the question and answer period, but I encourage the committee to do a few things.
One, investigate better protection of workers and workers' rights, including greater data protection and safeguards and enforcement against invasive workplace surveillance, especially to ensure workers can't train themselves out of a job.
Two, consider arbitration and greater support in bargaining power, especially for contracts between independent contractors and large technology firms.
Three, ensure that efficiency benefits are fairly distributed, such as considering a four-day workweek, raising minimum wage and ensuring a right to disconnect.
Thank you for the time and the opportunity to speak.
