Abstract
This research revolves around the potential challenges, opportunities, and strategies associated with human-centered generative artificial intelligence (AI) in the music compositional practice, emphasizing the role of metaphorical design in shaping musicians' expectations toward the adoption of generative AI in their everyday creative activities. Through a human-computer interaction (HCI) lens, this paper aims to discuss the cultural implications of the human-AI metaphorical design space for the seamless integration of intelligent algorithmic experiences in a manner that aligns with cultural values and realistic expectations of music creators while promoting informed policies, sociotechnical imaginaries, and culturally sensitive generative AI design strategies with focus on user-friendly interfaces that resonate with diverse music creation groups.

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Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities. © 2024 IEEE.

Abstract