Artificial Intelligence is transforming our world at an unprecedented pace. From self-driving cars to medical diagnosis, AI's potential seems limitless. However, with great power comes great responsibility. The ethical implications of AI development are profound and demand careful consideration from engineers, policymakers, and society as a whole. Bias in AI systems is a major concern. If training data reflects existing societal biases, AI models can perpetuate and even amplify these biases, leading to unfair or discriminatory outcomes. Ensuring fairness and equity in AI requires diverse datasets and rigorous testing. Transparency and explainability are also crucial. Many advanced AI models, particularly deep learning networks, operate as "black boxes," making it difficult to understand how they arrive at their decisions. This lack of transparency can be problematic, especially in critical applications like healthcare or criminal justice. Accountability is the third pillar of AI ethics. Who is responsible when an AI system makes a mistake or causes harm? Establishing clear lines of accountability is essential for building trust and ensuring that AI is developed and deployed responsibly.