The article discusses how adding causal reasoning to AI systems can make them more trustworthy and reliable. By incorporating causality into machine learning models, we can create better and more explainable models. This helps in understanding why a model makes certain decisions and is crucial for applications like fairness and transfer learning. By using causality in deep learning models, we can achieve models that are not only accurate but also provide clear explanations for their decisions.