Artificial intelligence has the potential to improve air traffic management through the consistent use of machine learning. AI can bring benefits to air traffic controllers in terms of workload, situational awareness, trust, and thus operational efficiency and safety. However, human problem-solving strategies can potentially collide with AI and lead to misunderstandings and a decrease in user acceptance of air traffic control systems. The proposed paper focuses on the design of the ML system, in particular providing insights and guidelines derived from results of recent field studies as they addressed the impacts of conformance and transparency on controller behaviour and survey responses. Several guidelines were distilled based on empirical insights obtained from experiments, feedback from controllers and workshop results. The guidelines are divided into different categories: ML/AI design, Personalization, Transparency, and HCI. The proposed paper also describes a contribution to a different use case to test the generalizability of the guidelines themselves, as well as a recent update in the explainability framework developed by a regulatory authority.