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Designing fair and unbiased AI systems requires addressing issues such as algorithmic bias, dataset bias, and model interpretability. Key considerations include ensuring diverse and representative training data, implementing fairness-aware algorithms, and conducting rigorous evaluation and testing to identify and mitigate biases.