This Chapter discusses the critical importance of explainable AI, emphasizing its relevance in multidisciplinary collaborations and compliance with regulatory standards for emotion and mood detection in applications used in sensitive fields such as healthcare. It explores how explainable AI can facilitate better understanding and trust among participants from diverse disciplines (e.g. psychology and healthcare), ensuring that AI systems are transparent and that their decision-making processes are clear. The Chapter also highlights the necessity of meeting specific regulatory requirements to safeguard user privacy and data security, particularly in healthcare applications with high stakes. Furthermore, it addresses the ethical challenges involved in developing NLP applications for emotion detection, focusing on issues such as data biases, the potential for data misuse, and the overarching need for ethically responsible AI development. The Chapter concludes by advocating for integrating ethical principles throughout the AI development lifecycle to promote fairness, accountability, and transparency in AI.