Why It's Important
Privacy is a fundamental right, and the rise of Artificial Intelligence (AI) creates new and complex challenges to protecting it. For a community or small organization, maintaining the privacy of its members, clients, and staff is a matter of trust and safety. An ethical approach to AI is critical because these systems can make decisions that have real-world impacts, from service eligibility to economic opportunities. A failure to manage privacy and AI ethics correctly can lead to data breaches, discriminatory outcomes, and a loss of community confidence, all of which undermine the stability and resilience needed for local economic development.
History
Canada’s privacy laws, like the federal Personal Information Protection and Electronic Documents Act (PIPEDA), were developed before the widespread use of AI. These laws are based on principles of consent, accountability, and necessity. As AI became more powerful, the federal government began to address the new challenges it posed, introducing principles for responsible AI and proposing new legislation. The history of this issue is a story of technology moving faster than policy, creating a critical need for organizations to adopt strong ethical principles to fill the gaps in the law.
Examples
AI and Privacy in Public Debate: The Sidewalk Labs project in Toronto, though ultimately canceled, sparked a major national conversation in Canada about data collection, privacy, and the use of AI in public spaces. It highlighted the need for strong, community-led governance before implementing “smart city” technologies.
Government AI Accountability: The Government of Canada uses an Algorithmic Impact Assessment (AIA) tool to assess the risks of its automated decision-making systems. The public registry of these assessments provides real-world examples of how federal departments are grappling with AI ethics and privacy.
Facial Recognition Policy: Public and police use of facial recognition technology has been heavily scrutinized by Canada’s privacy commissioners. The subsequent investigations and reports from bodies like the Office of the Privacy Commissioner of Canada serve as concrete examples of the ethical and privacy lines that must be considered.
Software and Tools
Ethical AI is about governance, not just technology. The most important tools are policies and frameworks.
Privacy Impact Assessment (PIA): A process promoted by the Office of the Privacy Commissioner of Canada. A PIA is a systematic way to identify and manage the privacy risks of any new project, which is essential before adopting any AI tool.
Algorithmic Impact Assessment (AIA): A tool developed by the Government of Canada to help organizations assess the risks and impacts of automated decision-making systems.
AI Usage Policy Template: A written document that your organization can create. It should state what AI tools are approved for use, what types of data (especially personal information) can NEVER be entered into them, and who is responsible for oversight.
Generative AI Tools with Privacy Controls: When using public AI tools like OpenAI, it’s crucial to use enterprise or “team” versions that have clearer privacy policies and often include options to prevent your data from being used to train the model.
AI Considerations
This entire section is about AI considerations. The key ethical risks with AI are:
Privacy: AI systems require vast amounts of data to be trained, creating a powerful incentive for organizations to collect more data than necessary, increasing privacy risks.
Bias and Discrimination: If an AI is trained on data that reflects historical biases (e.g., in hiring, policing, or lending), the AI will learn and amplify those biases, leading to unfair outcomes.
Transparency: It can be very difficult to understand why an AI made a particular decision. This “black box” problem makes it hard to challenge or correct errors.
Accountability: When an AI system causes harm, who is responsible? The developer? The organization that used it? Establishing clear lines of accountability is a major ethical challenge.
FAQ
Under Canadian law, it is any information about an identifiable individual. This includes obvious things like a name and address, but also less obvious things that, when combined, could identify someone.
AI bias occurs when an AI system produces results that are systematically prejudiced due to flawed assumptions in the machine learning process. It often comes from training the AI on biased data.
No, but you are still responsible for complying with all existing laws, including privacy laws. If your use of AI results in a privacy breach or discrimination, your organization can be held liable.
A PIA is a risk management process that helps you analyze how a new project or system will affect individual privacy and ensure that you are meeting your legal obligations.
You should always treat AI output with skepticism. It should be seen as a starting point or a draft, and all facts, figures, and important statements must be verified by a human.
Pro Tips
When working on AI projects, make privacy, transparency, and fairness central to your approach. Educate yourself about how data is collected, used, and stored, and seek informed consent from participants. Study how biased training data can lead to discriminatory outcomes, and practise auditing algorithms for fairness. Your commitment to ethical AI protects individuals’ rights and sets a standard for future work.
Checklist
External Resources
Office of the Privacy Commissioner of Canada: The primary resource for understanding your privacy obligations in Canada, with specific guidance on new technologies.
Digital Governance Council: A Canadian organization that works on developing standards for digital governance, including the ethical use of data and AI.
The Montreal Declaration for a Responsible Development of Artificial Intelligence: An influential ethical framework for the development and use of AI, developed through a collaborative process in Montreal.