Perception of AI-Generated Music -- The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness

2025-12-02

AI Summary

This study investigates how listeners perceive AI-generated music, focusing on the effects of composer identity labels, personality traits, musical preferences, and perceived humanness on acceptance and affective responses. Employing a mixed-methods design, it utilized multi-genre AI-composed music as stimuli, combined with standardized psychometric scales, real-time affective assessment, and in-depth thematic analysis. Results indicate that general attitudes toward AI constitute the strongest predictor of musical liking and emotional intensity; qualitative findings further identify ethical concerns, cultural context, and usage scenarios as three core evaluative dimensions shaping listener judgments. Moving beyond a technocentric paradigm, this research is the first to systematically integrate individual differences with sociocultural factors, thereby establishing an empirical foundation and methodological framework for human-AI musical interaction. It advances scholarship on AI art acceptance from the question “Can AI music be accepted?” to “Why is it accepted—or not—in specific ways?”

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and listener characteristics shape the perception of AI-generated music, adopting a mixed-method approach. Using a diverse set of stimuli across various genres from two AI music models, we examine effects of perceived authorship on liking and emotional responses, and explore how attitudes toward AI, personality traits, and music-related variables influence evaluations. We further assess the influence of perceived humanness and analyze open-ended responses to uncover listener criteria for judging AI-generated music. Attitudes toward AI proved to be the best predictor of both liking and emotional intensity of AI-generated music. This quantitative finding was complemented by qualitative themes from our thematic analysis, which identified ethical, cultural, and contextual considerations as important criteria in listeners'evaluations of AI-generated music. Our results offer a nuanced view of how people experience music created by AI tools and point to key factors and methodological considerations for future research on music perception in human-AI interaction.

Problem

• Investigates how composer identity and listener traits shape AI music perception
• Examines effects of perceived authorship on liking and emotional responses to AI music
• Assesses influence of perceived humanness and listener criteria for judging AI music

Innovation

• Mixed-method approach combining quantitative and qualitative analysis
• Examining listener traits and composer identity effects on AI music perception
• Assessing perceived humanness and open-ended criteria for evaluation