AI Glossary
Bias (in AI)
Systematic errors in AI outputs caused by skewed training data or flawed model design. Bias can lead to unfair hiring recommendations, loan approvals, or customer service experiences.
Understanding Bias (in AI)
AI bias isn't a hypothetical risk — it has real business consequences. If your hiring AI was trained primarily on resumes from one demographic, it may systematically underrank qualified candidates from other groups, exposing your company to legal liability and missed talent.
Bias enters AI systems through training data (historical data reflecting past discrimination), model design choices (what features are weighted), and deployment context (applying a model outside its training domain).
Mitigating bias requires ongoing monitoring, diverse training data, regular audits, and human oversight on high-stakes decisions. It's not a one-time fix but a continuous process built into your AI governance framework.
Bias (in AI) in Canada
Canada's proposed Artificial Intelligence and Data Act (AIDA) will require businesses to assess and mitigate bias in high-impact AI systems, with penalties for non-compliance.
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Frequently Asked Questions
Regular audits that compare AI outcomes across demographic groups, monitoring output distributions over time, and using explainability tools like SHAP to understand which factors drive decisions.
No. Commercial models can still produce biased outputs depending on your prompts, data, and use case. Businesses remain responsible for monitoring and mitigating bias in their specific applications.
See Bias (in AI) in Action
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