Next generation communication networks will rely heavily on Artificial Intelligence (AI) and Machine Learning (ML). In the pursuit of enhancing predictive performance, the imperative for transparency in AI/ML models has become paramount for successful deployment. Simultaneously, stringent privacy requirements underscore the need to safeguard user data and prevent unauthorized access. Thus, deploying AI systems necessitates a harmonious integration of transparency, privacy, and performance. However, achieving these objectives can be intricate, as they occasionally stand in tension with one another. For instance, increasing transparency may require revealing more information about how an AI model makes decisions, which could potentially compromise some sensible data. Simultaneously, prioritizing privacy may hinders the quality of data or even require limiting the amount of data that an AI model can access, which could impact its performance. In essence, while transparency, privacy, and performance are indispensable goals, their alignment demands a delicate equilibrium to ensure effective collaboration in the design of AI-based algorithms for communication networks, steering clear of a potential dark triad.