AI agents prevent biased decisions primarily through rigorous data preprocessing, ensuring training datasets are diverse, representative, and free from historical biases. This involves techniques like data augmentation and re-weighting to balance representation across different demographic groups. Furthermore, they employ fairness-aware algorithms and bias detection metrics during model development to actively identify and mitigate unfair outcomes. Explainable AI (XAI) methods are also crucial, providing transparency into decision-making processes and allowing developers to pinpoint sources of potential bias. Continuous monitoring and auditing of agent performance in real-world scenarios, coupled with human oversight and feedback loops, helps detect and correct emergent biases post-deployment. These multi-faceted approaches aim to build agents that make more equitable and just decisions. More details: https://realdom.com.ua/