Thank you Mr, Chair and members of the committee.

It is a privilege to be here today to present the industry's perspective on behalf of the Information Technology Association of Canada. ITAC is the national voice of the telecommunications and Internet technology industry. We have more than 300 members, including more than 200 small and medium-sized businesses.

As already noted by the other speakers, there's a lot of promise and opportunity behind artificial intelligence to support economic growth and societal improvements, and the opportunities are seemingly boundless. From human mobility by automating vehicles to precision health care, many of our forthcoming solutions will be powered by artificial intelligence.

To realize the full benefits of artificial intelligence, we'll need to create systems that people trust. I've provided a brief outline of the slides that I'll present here today, including our industry's obligations and where our industry is already going; a call on our government to lead in terms of developing an ongoing dialogue via public-private partnership; the types of impacts that this will have on our workforce and the need for re-skilling, upskilling and training; and the recommendations in order to build trust in artificial intelligence.

Canada has been recognized as a global leader in artificial intelligence research and development. We are attracting global talent to universities across Canada to study in this field. We're already experiencing the benefits of AI in a number of fields, from start-ups and SMEs to larger global tech companies, all of which have developed AI systems to help solve businesses' or some of society's most pressing problems. Many others are using AI to improve supply chain efficiencies, to advance public services and to advance groundbreaking research. By leveraging large datasets, increased computing power and ingenuity, AI-driven solutions can address any number of societal or business problems, from precision or predictive health care to automated and connected vehicles improving human mobility and decreasing traffic, having an exponential impact on our environment.

AI systems need to leverage vast amounts of data. The availability of robust and representative data, often de-identified or anonymized, is required for building and improving AI and machine learning systems. We can't overstate this enough: Having access to broad and vast amounts of data is the key to advancing our artificial intelligence capabilities in Canada.

That said, the AI ecosystem is global. It's very competitive and it's multi-faceted. Our association welcomes a multi-stakeholder engagement approach to artificial intelligence, one that encourages Canada to bolster global engagement on AI policy to ensure we are all prospering from the potential benefits for our societies.

I'll note six key factors for the committee to consider.

First, traditional industries are already seizing and leading in AI opportunities. From oil and gas to mining, forestry and agriculture, they are embracing this technology to drive efficiencies and compete on a global scale. They are developing new services and new products based on the information being analyzed and leveraging artificial intelligence.

Second, AI is a journey. This isn't going to be an end state. This is going to be something that continues to evolve over the forthcoming decades.

Third, central to any economy's digital transformation is cultural transformation, and misinformation in this space will kill consumer and citizen trust in new technology and artificial intelligence.

Fourth, there will be workforce disruption, but based on historical factors, we believe new technologies including AI will create more job opportunities than it will kill.

Fifth, we need partnerships for workforce development, including the re-skilling and upskilling of existing people who may force disruption, based on their current roles.

Sixth, next-generation policies are needed. These are next-generation technologies. It's time for us to start thinking outside the box.

When I first joined government in 1999, one of the first jobs I had was working to support the development of PIPEDA. I was also one of the lead architects of Canada's anti-spam legislation. I did my master's thesis on why SMEs struggle to comply with CASL and PIPEDA, so I've been working on this for the better part of the last 17 or 18 years. Interestingly, we never foresaw the impact that data would have on the legislative frameworks we have today. We couldn't foresee, when developing PIPEDA or CASL, the types of data-driven businesses that have come our way to date.

Next, I want to talk about industry's obligation to promote responsible development and use of artificial intelligence.

First, we recognize our responsibility to integrate principles and values into the design of AI technologies, beyond compliance with existing laws. While the potential benefits to people in society are amazing, AI researchers, subject-matter experts and stakeholders should continue to spend a great deal of time working to ensure the responsible design and deployment of AI systems, including addressing safety and controllability mechanisms, the use of robust and representative data, enabling greater interpretability and recognizing that solutions must be tailored to the unique risks presented by the specific context in which a particular system operates.

Second, in terms of safety, security, controllability and reliability, we believe technologists have a responsibility to ensure the safe design of AI systems. Autonomous AI agents must treat the safety of users and third parties as a paramount concern, and AI technology should strive to reduce risks to humans. Furthermore, the development of autonomous AI systems must have safeguards to ensure controllability of the AI systems by humans, tailored to the specific context in which a particular system operates.

Third is robust and representative data, with a specific focus on mitigating bias. To promote the responsible use of data and to assure its integrity at every stage, industry has a responsibility to understand the parameters and characteristics of the data, to demonstrate the recognition of potentially harmful bias and to test for potential bias before and throughout the deployment of AI systems.

AI systems need to leverage large datasets. The availability of robust and representative data for building and improving AI and machine learning systems is of utmost importance.

By the way, this could be a significant competitive advantage for Canada. We have a globally representative population, including indigenous communities. It would be a wonderful target for medical testing and AI testing in the medical field.

In terms of interpretability, we should leverage public-private partnerships to find ways to better mitigate bias, inequity and other potential harms in automated decision-making systems. Our approach to finding such solutions should be tailored to the unique risks presented by the specific context in which a particular system operates.

Finally, the use of AI to make autonomous consequential decisions about people informed by, but often replacing, decisions made by humans has led to concerns about liability. Acknowledging existing legal and regulatory frameworks, our industry is committed to partnering with relevant stakeholders to form a reasonable accountability framework for all entities in the context of automated systems.

We believe we should leverage and build a public-private partnership that can expedite AI R and D, democratize access to data, prioritize diversity and inclusion and prepare our workforce for the jobs of the future. ITAC members also believe that we need to prioritize an effective and balanced liability regime via the continued engagement of multi-stakeholder expert groups. The right solution is only going to come from an open exchange with all actors in the AI supply chain.

If the value favours only certain incumbent entities, there's a risk of exacerbating existing wage income and wealth gaps. In this scenario, this isn't “us versus them”, “private versus public”. It's just “us”. There should be increased partnership to explore how to develop a safer and more secure and trusted data-driven digital economy.

There is a concern that AI will result in job change, job loss and worker displacement. While these concerns may be understandable, it should be noted that most emerging AI technologies are designed to perform a specific task or to assist and augment a human's capacity rather than to replace a human employee. This type of augmented intelligence means that a portion—most likely not all—of an employee's job could be replaced or made easier by AI.

Leveraging AI to complete an employee's menial tasks is a way to increase their productivity by freeing up time to engage in customer service and interaction or more value-added job functions. Nevertheless, while the full impact of AI on jobs is not yet fully known in terms of both jobs created and jobs displaced, an ability to adapt to rapid technological change is critical. We should leverage traditional human-centred resources as well as career educational models, and newly developed AI technologies should assist in developing both the existing workforce and the future workforce to help Canadians navigate through career transitions.

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