Searching for Human Bias Against AI-Composed Music

Dimiter Zlatkov, Jeff Ens, Philippe Pasquier

Abstract: With the popularization of musical AI in society comes the question of how it will be received by the public. We conducted an empirical study to investigate the hypotheses that human listeners hold a negative bias against computer-composed music. 163 participants were recruited from Amazon’s MTurk to fill out a survey asking participants to rank 5 computer-composed and 5 human-composed musical excerpts based on subjective musical preference. Participants were split into two groups, one informed of correct authorship, the other deceived. The hypothesis, that those in the informed group would rank computer-composed excerpts as lower than human-composed excerpts, was not supported by significant results. We outline potential weaknesses in our design and present possible improvements for future work. A review of related studies on bias against AI-composed music and art is also included.