Fairness and transparency are fundamental pillars of trustworthy AI systems, especially as AI is increasingly used in sensitive domains such as healthcare, education, finance, and justice. Fairness in AI means that algorithmic decisions should not discriminate against particular groups and should be based on objective and balanced criteria. However, fairness is not automatic, because AI models learn from data, and if training data contains historical or structural bias, the system may reproduce and amplify that bias in its outcomes. Transparency refers to the ability to understand how an AI system works and how it reaches its decisions. Highly complex models — especially deep neural networks — often behave like black boxes, making their internal reasoning difficult to interpret. This challenge has led to the growth of explainable AI methods that aim to make model behavior more understandable to users, auditors, and regulators. Transparency is essential for trust, accountability, and error correction. Ensuring fairness and transparency requires practical measures such as dataset auditing, diverse evaluation benchmarks, development documentation, and multidisciplinary oversight. Organizations should clearly disclose when and how AI systems are used. Fair and transparent AI not only builds public confidence but also improves decision quality and reduces social risk.