AAHIM | What Is AI Bias and How Do I Spot It?
Artificial intelligence is increasingly relied upon to inform decisions, prioritize actions, and automate complex workflows, but bias in AI systems often appears subtly rather than as obvious errors. Instead, it shows up in confident recommendations built on incomplete, skewed, or misrepresented data. This session explores how bias enters AI systems across the full lifecycle from data collection and labeling to model design, deployment, and user interaction and how it creates recognizable warning signs in AI outputs, such as unexplained performance gaps, overconfident predictions, and results that reinforce existing narratives.
