Engineering communities that feed the current proliferation of artificial intelligence (AI) have historically been slow to recognise the spectrum of societal impacts of their work. Frequent controversies around AI applications in creative domains demonstrate insufficient consideration of ethical predicaments, but the abstract principles of current AI and data ethics documents provide little practical guidance. Pragmatic methods are urgently needed to support developers in ethical reflection of their work on creative-AI tools. In the wider context of value sensitive, people-oriented design, we present an analytical method that implements an ethically informed and power-sensitive stakeholder identification and mapping: Ethically Aligned Stakeholder Elicitation (EASE). As a case study, we test our method in workshops with six research groups that develop AI in musical contexts. Our results demonstrate that EASE supports critical self-reflection of the research and outreach practices among developers, discloses power relations and value tensions in the development processes, and foregrounds opportunities for stakeholder engagement. This can guide developers and the wider NIME community towards ethically aligned research and development of creative-AI.