Yes, different fairness measures can sometimes conflict with each other in AI development. This happens because fairness is a complex and multifaceted concept, and there are various ways to define and measure it. Different fairness measures may prioritize different aspects of equity, leading to trade-offs. For example, one fairness measure might focus on individual fairness, which ensures that similar individuals are treated similarly, while another might focus on group fairness, which aims to balance outcomes across different demographic groups. Balancing these two measures can be challenging because improving fairness for one group may negatively impact the fairness for another. Additionally, fairness can be measured in terms of equality of opportunity or equality of outcome, and these approaches can sometimes be at odds. Equality of opportunity aims to give individuals an equal chance to succeed, while equality of outcome strives for equal results, which can conflict when the distribution of resources or opportunities is uneven. In such cases, it is essential to carefully consider the context and the goals of the AI system to determine the most appropriate fairness measure or to find an acceptable balance between conflicting measures.