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Artificial Intelligence Tip Sheet (view source)
Revision as of 14:10, 24 September 2025
, Yesterday at 14:10no edit summary
* Data Integrity: The quality and fairness of AI systems depend entirely on the quality and fairness of the data it's trained on.
* Data Integrity: The quality and fairness of AI systems depend entirely on the quality and fairness of the data it's trained on.
* Accountability & Redress: Clear mechanisms must exist for people to challenge AI-driven decisions and seek recourse.
* Accountability & Redress: Clear mechanisms must exist for people to challenge AI-driven decisions and seek recourse.
=== 1. Validating & Managing Data ===
AI systems learn from data. If this data reflects historical or societal bias, the AI will learn, perpetuate, and even amplify that bias.
* Audit Your Data Sources: Before any development, rigorously analyze your datasets for historical biases. For example, if historical data shows a certain demographic was disproportionately denied a service, using that data without correction will teach the AI to continue that discriminatory pattern.
* Ensure Data Representativeness: Ensure data reflects the diversity of people in Canada. If there are gaps (e.g., underrepresentation of Northern communities or persons with disabilities), develop a strategy to address them before proceeding.
* Practice Data Minimization: Only collect and use the data that is absolutely necessary for the system’s purpose. Every extra data point increases the risk of introducing bias and privacy violations.
* Establish Clear Data Governance: Appoint clear ownership and accountability for the data's quality, lifecycle, and ethical use.