Fairness is paramount in the development and deployment of generative AI. Bias in training data can lead to AI systems that perpetuate and even amplify existing societal inequalities. Ensuring fairness requires a multifaceted approach, starting with careful curation of training datasets to minimize representation bias. This involves actively seeking diverse and representative data sources, and employing techniques to detect and mitigate biases within the data itself. Beyond data, algorithmic fairness is crucial. Algorithms themselves can introduce biases, even with unbiased data, through design choices or the way they learn from data. Therefore, careful algorithm design and evaluation are necessary. Furthermore, ongoing monitoring and evaluation of deployed AI systems are essential to identify and address emerging fairness issues. Transparency is key; understanding how a generative AI system makes decisions allows for better identification and remediation of fairness problems. Finally, fairness considerations must extend to the broader societal impact of the AI, considering potential downstream effects on different groups. A collaborative and interdisciplinary approach, involving AI researchers, ethicists, and social scientists, is crucial for achieving fairness in generative AI.