The implications of AI bias in automated systems can lead to unfair, inconsistent, and discriminatory outcomes. Biases can occur during the data collection process, model training, or algorithm design, often reflecting societal inequalities. This can result in misleading predictions or recommendations, which might adversely affect marginalized groups. Addressing AI bias is crucial for fairness, accountability, and compliance with ethical standards, and requires ongoing scrutiny and refinement in development processes.