Integrating AI can be a successful initiative that propels your organization toward meaningful growth. However, it is essential to remain vigilant about the associated risks to prevent them from compromising your earnings and turning what could be a profit into a disastrous loss.

Therefore, I offer you an overview of the risks to monitor carefully to maximize your chances of success.

• Inaccuracies: AI relies heavily on data for its machine learning processes. The principle of “Garbage in, Garbage out” is particularly relevant here; the quality of AI results is directly related to the quality of incoming data. If the data contains errors, or biases or does not accurately represent reality, AI models will incorporate these flaws into their predictions or decisions.

• Systemic bias: AI is not immune to human bias because it learns from data that may be biased. For example, a recruitment algorithm can continue perpetuating discrimination if it learns from discriminatory historical data. Recently, a case of systemic bias attracted media attention, with a credit pre-approval system disproportionately rejecting women’s applications. This occurred because the system primarily relied on data from male borrower profiles.

• Privacy: as data collection increases, it becomes essential to manage this information securely. Companies must ensure compliance with strict regulations such as the GDPR in Europe or Law 25 in Quebec. There have been instances where some systems have accidentally exposed training data, including confidential information and personal data. In this context, the model alignment principle is crucial. It refers to the need to ensure that the system acts in a manner consistent with the intentions of human operators.

• Regulatory compliance: AI laws are changing rapidly. Organizations must stay informed of the latest legislation. Canada is making active progress in implementing comprehensive legislation to regulate AI use and strengthening privacy laws through Bill C-27. This bill proposes Canada-wide requirements for AI system design, development, use and supply.

• Opt-out in model training: customers must have the option to opt out of training product AI models with their data. This minimizes the risk of public disclosure of their data should a malicious entity compromise the training data.

• Implementation costs: although less and less expensive to implement, efforts to properly configure AI should not be underestimated to ensure that the results provided are accurate or precise enough to generate benefits. Errors can have significant impacts.

• Staff skills: without necessarily building a team of AI experts, it is essential to have staff who understand the risks, benefits and key AI concepts to interact and supervise suppliers effectively.

• Ethical considerations: AI system decisions must be fair, transparent and understandable, especially when they affect vital aspects of daily life. In this context, the importance of the alignment principle and appropriate configuration becomes critical.