Definition of Bias: Bias in AI refers to systematic errors that can occur in the model due to prejudiced assumptions made during the data collection, model training, or deployment stages.

Impact on Outcomes: Bias can cause AI systems to produce unfair, discriminatory, or incorrect results, which can have serious ethical and legal implications. For example, biased AI in hiring systems can disadvantage certain demographic groups.

Mitigation Strategies: Efforts to mitigate bias include diversifying training data, implementing fairness-aware algorithms, and conducting regular audits of AI systems.