There are two key ways that Canada’s AI sector should diversify its workforce in order to remain competitive.
Artificial intelligence (AI) is expected to add US$15.7 trillion to the global economy by 2030, according to a recent report from PwC, representing a 14 percent boost to global GDP. Countries around the world are scrambling for a piece of the pie, as evidenced by the proliferation of national and regional AI strategies aimed at capturing the promise of AI for future value generation.
Canada has benefited from an early lead in AI, which is often attributed to the Canadian Institute for Advanced Research (CIFAR) having had the foresight to invest in Geoffrey Hinton’s research on deep learning shortly after the turn of the century. As a result, Canada can now tout Montreal as having the highest concentration of researchers and students of deep learning in the world and Toronto as being home to the highest concentration of AI start-ups in the world.
But the market for AI is approaching maturity. A report from McKinsey & Co. suggests that the public and private sectors together have captured only between 10 and 40 percent of the potential value of advances in machine learning. If Canada hopes to maintain a competitive advantage, it must both broaden the range of disciplines and diversify the workforce in the AI sector.
Looking beyond STEM
Strategies aimed at capturing the expected future value of AI have been concentrated on innovation in fundamental research, which is conducted largely in the STEM disciplines: science, technology, engineering and mathematics. But it is the application of this research that will grow market share and multiply value. In order to capitalize on what fundamental research discovers, the AI sector must deepen its ties with the social sciences.
To date the role of social scientists in Canada’s strategy on AI has been largely limited to areas of ethics and public policy. While these are endeavours to which social scientists are particularly well suited, they could be engaged much more broadly with AI. Social scientists are well positioned to identify and exploit potential applications of this research that will generate both social and economic returns on Canada’s investment in AI.
Social scientists take a unique approach to data analysis by drawing on social theory to critically interpret both the inputs and outputs of a given model. They ask what a given model is really telling us about the world and how it arrived at that result. They see potential opportunities in data and digital technology that STEM researchers are not trained to look for.
A recent OECD report looks at the skills that most distinguish innovative from non-innovative workers; chief among them are creativity, critical thinking and communication skills. While these skills are by no means exclusively the domain of the social sciences, they are perhaps more central to social scientific training than to any other discipline.
The social science perspective can serve as a defence mechanism against the potential folly of certain applications of AI. If social scientists had been more involved in early adaptations of computer vision, for example, Google might have been spared the shame of image recognition algorithms that classify people of colour as animals (they certainly would have come up with a better solution). In the same vein, Microsoft’s AI chatbots would have been less likely to spew racist slurs shortly after launch.
Social scientists can also help meet a labour shortage: there are not enough STEM graduates to meet future demand for AI talent. Meanwhile, social science graduates are often underemployed, in part because they do not have the skills necessary to participate in a future of work that privileges expertise in AI. As a consequence, many of the opportunities associated with AI are passing Canada’s social science graduates by. Excluding social science students from Canada’s AI strategy not only reduces their career paths but restricts their opportunities to contribute to fulfilling the societal and economic promise of AI.
Realizing the potential of the social sciences within Canada’s AI ecosystem requires innovative thinking by both governments and universities. Federal and provincial governments should relax restrictions on funding for AI-related research that prohibit applications from social scientists or make them eligible only within interdisciplinary teams that include STEM researchers. This policy has the effect of subordinating social scientific approaches to AI to those of STEM disciplines. In fact, social scientists are just as capable of independent research, and a growing number are already engaged in sophisticated applications of machine learning to address some of the most pressing societal challenges of our time.
Governments must also invest in the development of undergraduate and graduate training opportunities that are specific to the application of AI in the social sciences, using pedagogical approaches that are appropriate for them.
Social science faculties in universities across Canada can also play a crucial role by supporting the development of AI-related skills within their undergraduate and graduate curriculums. At McMaster University, for example, the Faculty of Social Sciences is developing a new degree: master of public policy in digital society. Alongside graduate training in the fundamentals of public policy, the 12-month program will include rigorous training in data science as well as technical training in key digital technologies that are revolutionizing contemporary society. The program, which is expected to launch in 2021, is intended to provide students with a command of digital technologies such as AI necessary to enable them to think creatively and critically about its application to the social world. In addition to the obvious benefit of producing a new generation of policy leadership in AI, the training provided by this program will ensure that its graduates are well positioned for a broader range of leadership opportunities across the public and private sectors.
Increasing workplace diversity
A report released in 2019 by New York University’s AI Now Institute declared that there is a diversity crisis in the AI workforce. This has implications for the sector itself but also for society more broadly, in that the systemic biases within the AI sector are being perpetuated via the myriad touch points that AI has with our everyday lives: it is organizing our online search results and social media news feeds and supporting hiring decisions, and it may even render decisions in some court cases in future.
One of the main findings of the AI Now report was that the widespread strategy of focusing on “women in tech” is too narrow to counter the diversity crisis. In Canada, efforts to diversify AI generally translate to providing advancement opportunities for women in the STEM disciplines. Although the focus of policy-makers on STEM is critical and necessary, it is short-sighted. Disciplinary diversity in AI research not only broadens the horizons for research and commercialization; it also creates opportunities for groups who are underrepresented in STEM to benefit from and contribute to innovations in AI.
As it happens, equity-seeking groups are better represented in the social sciences. According to Statistics Canada, the social sciences and adjacent fields have the highest enrolment of visible minorities. And as of 2017, only 23.7 percent of those enrolled in STEM programs at Canadian universities were women, whereas women were 69.1 percent of participants in the social sciences.
So, engaging the social sciences more substantively in research and training related to AI will itself lead to greater diversity. While advancing this engagement, universities should be careful not to import training approaches directly from statistics or computer science, as these will bring with them some of the cultural context and biases that have resulted in a lack of diversity in those fields to begin with.
Bringing the social sciences into Canada’s AI strategy is a concrete way to demonstrate the strength of diversity, in disciplines as well as demographics. Not only would many social science students benefit from training in AI, but so too would Canada’s competitive advantage in AI benefit from enabling social scientists to effectively translate research into action.
Do you have something to say about the article you just read? Be part of the Policy Options discussion, and send in your own submission. Here is a link on how to do it. | Souhaitez-vous réagir à cet article ? Joignez-vous aux débats d’Options politiques et soumettez-nous votre texte en suivant ces directives.