AI bias and fairness are critical dimensions of responsible AI that address how machine learning systems may produce discriminatory outcomes based on protected attributes like race, gender, or age. Bias emerges from multiple sources — biased training data, flawed algorithmic design, or problematic evaluation methods — and manifests as systematic unfairness toward specific demographic groups. Fairness aims to ensure equitable treatment and outcomes through mathematical constraints, mitigation techniques, and ongoing monitoring, though trade-offs often exist between different fairness definitions and between fairness and accuracy. Understanding these concepts is essential because biased AI systems can perpetuate and amplify societal inequalities in high-stakes domains like hiring, lending, healthcare, and criminal justice.