Data bias is one of AI's biggest limitations, coming from several sources with serious effects on user experience. Bias enters AI systems mainly through training data that doesn't properly represent certain groups or contains historical prejudices:

• Selection bias happens when data collection methods leave out certain populations, like a speech recognition system trained mostly on native English speakers that struggles with accents.
• Confirmation bias occurs when systems are tuned toward expected outcomes, such as a hiring algorithm that favors candidates resembling previously successful employees.
• Measurement bias emerges when the metrics used don't truly reflect real-world goals, like a content recommendation system optimized for clicks rather than user satisfaction.

Addressing data bias requires deliberate effort from everyone involved in AI development and implementation.

Product managers need to prioritize fairness in requirements, data scientists must carefully evaluate training datasets for representation gaps, developers should implement diverse testing methods, and designers need to create feedback systems that capture different perspectives. Together, these professionals must build clear indicators when systems operate with known limitations or uncertainties.