Graphs are a natural fit for understanding relationships in data, as they were built to incorporate connections and enrich data with value. In nature, you don't get isolated data points; instead, graphs help us understand the fabric of relationships in data. This context is essential for answering questions like how to navigate between points or find efficient routes. Graphs can also add more value by incorporating additional context, such as companies' innovations and user requests. The intersection of responsible AI and graph technology is crucial, particularly in areas like robustness, trustworthiness, and explainability. By using graphs, we can extract relationships and community detection to identify fraud, improve autonomous decision-making, track data lineage, and reveal bias in the data itself. As AI adoption accelerates, it's essential to consider human values and societal implications, as seen in graph technology's increasing traction in various use cases like drug discovery, financial crimes, and predictive maintenance. Practical tips for responsible AI include debiasing data, involving experts, using developer resources, and adding context with knowledge graphs. Ultimately, graphs enable us to address shortcomings in technically based AI systems and human flaws, making them a crucial component of the future of AI.