Development of data-driven tools for artistic creation touches upon a diverse range of stakeholders, but it is insufficiently recognised in the engineering communities working with creative technologies. To support increased awareness and sensitivity to ethical predicaments of the development work, we present an analytical for a structured, power-sensitive stakeholder identification and mapping – Ethically Aligned Stakeholder Elicitation (EASE). As a case study, we test the method in workshops with six groups that develop artificial intelligence in musical contexts (music-AI). The results from the workshops demonstrate that methods like EASE can effectively promote critical self-reflection and expose value tensions in the development processes, thus helping developers move towards ethically aligned research and development of creative-AI.