Artificial Intelligence is no longer a subject of science fiction. It is being used in many fields to perform an infinite number of tasks. Some even use AI to create music. This paper examines the fundamental inability of artificial intelligence systems to produce authentic protest music, building on critical discussions of human subjectivity, cultural memory, and political expression in vernacular and popular music. Drawing on the work of R. Serge Denisoff and Mark H. Levine, alongside scholarship in the sociology of music and cultural theory, in addition to the author’s personal experience as a songwriter, this argument demonstrates that protest music depends on irreducibly human qualities: lived experience, moral consciousness, historical embeddedness, collective empathy, and intentional political resistance. While AI music generation tools can mimic formal and stylistic elements of protest songs—lyrical patterns, chord progressions, vocal tone, and thematic vocabulary—they cannot replicate the subjective, emotional, and social foundations that give such music meaning and transformative power. By analyzing representative protest songs across the twentieth and twenty first centuries, this paper shows how authentic protest music – human-made music -- emerges from specific historical struggles. Meanwhile AI-composed music remains a decontextualized, apolitical hollow or “uncanny imitation” This study argues that human subjectivity is not an incidental feature of political music; it is its very condition of possibility.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
1. The Absence of Lived Struggle: AI Cannot Feel Injustice
Protest music originates in the raw, unmediated experience of oppression. Its power stems from the artist’s direct encounter with injustice—whether racial violence, economic exploitation, war, or systemic inequality—and the urgent need to give voice to that pain. As R. Serge Denisoff, a pioneer in protest music studies, observes, "Protest songs are not merely compositions; they are crystallizations of lived suffering, forged in the fire of real-world conflict"
[3]
Denisoff, R. Serge. "Folk-Rock: Folk Music, Protest, or Commercialism?" The Journal of Popular Culture, vol. 3, no. 2, 1969, pp. 214-228.
Bob Dylan did not write Blowin’ in the Wind by analyzing folk song patterns; he wrote it from the frontlines of the civil rights movement, witnessing segregation and police brutality. Nina Simone’s Mississippi Goddam was a visceral reaction to the 1963 bombing of the 16th Street Baptist Church, a personal and collective trauma.
AI, by contrast, has no body, no history, no stake in human struggle. It processes data about protest music—lyrical themes, chord progressions, vocal inflections—but cannot experience the injustice it purports to critique. As Denisoff and Mark H. Levine argue in their critique of popular music analysis, "Lyrics alone are only part of the total sound; the emotional weight comes from the lived experience behind them"
[5]
Denisoff, R. Serge, and Mark H. Levine. "The One Dimensional Approach to Popular Music: A Research Note." Journal of Popular Culture, vol. 4, no. 4, 1971, pp. 911-918.
. AI can generate lyrics about "police brutality" or "racial inequality," but these words are statistical recombinations, not expressions of genuine grief or rage.
This absence of lived experience creates a fatal disconnect. A 2025 study in Music, Sound, and the Moving Image found that audiences consistently rated AI-generated protest songs as "technically skilled but emotionally flat," noting a lack of "urgency" and "moral weight" compared to human-created works
[1]
Barker, Thomas. "Music, Civil Rights, and Counterculture: Critical Aesthetics and Resistance in the United States, 1957-1968." Durham e-Theses, Durham University, 2016.
The uncanny valley effect applies here: the closer AI gets to mimicking human protest music, the more obvious its inauthenticity becomes. As philosopher Martha Nussbaum argues, art requires "emotional intelligence rooted in embodied experience"—a capacity AI, as a disembodied algorithm, fundamentally lacks
[8]
Nussbaum, Martha C. Upheavals of Thought: The Intelligence of Emotions. Cambridge University Press, 2001.
[8]
.
2. Moral Conviction and Political Purpose: AI Lacks a Stake in Justice
Protest music is inherently teleological: it exists to change the world. It is not just a form of expression but a political tool—a call to action, a rallying cry, a demand for justice. Martin Luther King Jr. called music "the soul of the civil rights movement" because it did more than reflect struggle; it fueled it, uniting protesters in shared purpose and resilience
[6]
Eyerman, Ron, and Andrew Jamison. Music and Social Movements: Mobilizing Traditions in the Twentieth Century. Cambridge University Press, 1998.
[6]
. Songs like We Shall Overcome were not passive anthems but active instruments of solidarity, sung in marches, jails, and churches to sustain courage and collective will.
This political purpose is inseparable from the artist’s moral conviction—a belief in the justice of the cause and a willingness to stand with the marginalized. As Denisoff explains in his analysis of 1960s protest music, "The most powerful protest songs come from artists who take risks, who face censorship, violence, and ostracism for their work"
[3]
Denisoff, R. Serge. "Folk-Rock: Folk Music, Protest, or Commercialism?" The Journal of Popular Culture, vol. 3, no. 2, 1969, pp. 214-228.
. Human protest artists take these risks because they have a personal stake in the struggle; their music is an act of courage, a refusal to be silent in the face of evil.
AI, however, has no moral agency, no values, no sense of right and wrong. It cannot "believe" in justice or "care" about oppression. It generates protest music not out of conviction but because it has been trained to recognize patterns associated with the genre. As LeVine notes in his analysis of music and revolution in the Arab Spring, "The power of protest music lies not just in what it says, but in who says it and why"
[7]
LeVine, Mark. "Music and the Aura of Revolution." International Journal of Middle East Studies, vol. 44, no. 4, 2012, pp. 455-472.
. An AI-generated "anthem" cannot inspire collective action because it does not come from a place of genuine solidarity. It is a hollow gesture, a simulation of resistance without the reality of commitment.
3. Collective Solidarity and Embodied Performance: AI Cannot Connect Human to Human
Protest music is a communal act, not a solitary one. It thrives in collective spaces—marches, rallies, folk clubs, street corners—where it is sung with others, not just at them. Its power lies in its ability to forge solidarity: to turn individual grief into collective strength, to make the marginalized feel seen and united. As Thomas V. Reed argues in The Art of Protest, "Protest music creates a shared emotional space where strangers become comrades, where individual suffering becomes collective struggle"
[10]
Reed, Thomas Vernon. The Art of Protest: Culture and Activism from the Civil Rights Movement to the Present. University of Minnesota Press, 2019.
This communal dimension is deeply embodied. Protest music is felt in the body: in the rhythm of a march, the vibration of a crowd singing in unison, the physical act of raising one’s voice in defiance. Performers do not just "sing" protest songs; they embody the struggle—their gestures, tone, and presence conveying the urgency of the moment. As Street observes in his study of protest music and social movements, "The physical act of singing together creates a shared identity that transcends individual differences"
[13]
Street, John. Music and Politics. Polity Press, 2011.
[13]
.
AI cannot replicate this embodied, communal connection. It generates music as a discrete product, not as a living, interactive practice. An AI cannot stand in a crowd of protesters, feel their energy, and adapt its performance in real time. It cannot respond to the mood of a movement, improvise lyrics to reflect unfolding events, or connect with an audience through shared struggle. As Rabaka notes in his analysis of Black Power music, "Protest music is a dialogue between artist and community, a conversation that AI cannot participate in
[9]
Rabaka, Reiland. Black Power Music! Protest Songs, Message Music, and the Black Power Movement. Lexington Books, 2022.
[9]
.
Moreover, protest music is shaped by oral tradition—it evolves through collective adaptation, with lyrics and melodies changing to reflect new struggles and contexts. We Shall Overcome began as a labor hymn, was adapted by civil rights activists, and continues to evolve today, its meaning reshaped by each generation that sings it
[4]
Denisoff, R. Serge. Protest Songs in America. Transaction Publishers, 1968.
[4]
AI, bound by its training data, cannot participate in this living tradition. It can only replicate existing forms, not grow and change with the movement it claims to represent.
4. Radical Originality: AI Cannot Break the Mold of the Known
The most powerful protest music is radically original—it does not just follow existing patterns but breaks them, forging new sonic and lyrical languages to express unheard struggles. Punk rock emerged as a protest against the complacency of 1970s rock, its raw, aggressive sound a rejection of polished commercialism. Hip-hop began as a voice for Black and Brown youth in the Bronx, using sampling, spoken word, and beat-making to challenge systemic racism and poverty
[12]
Rose, Tricia. Black Noise: Rap Music and Black Culture in Contemporary America. Wesleyan University Press, 1994.
[12]
.
These movements did not come from optimizing existing music; they came from disruption—from artists rejecting the status quo and inventing new forms to speak truth to power. As music critic Simon Reynolds notes, "The history of protest music is the history of breaking rules—and AI, which only knows the rules, can never be a rule-breaker"
[11]
Reynolds, Simon. Rip It Up and Start Again: Postpunk 1978-1984. Penguin Books, 2005.
[11]
.
AI, by its very nature, is a conservative force. It is trained on existing data—on the protest music that has already been created—and its output is a recombination of those patterns. It can blend folk, rock, and hip-hop to create something that sounds "new," but this novelty is not true originality. True originality in protest music is about transcending the known, about giving voice to experiences that have never been articulated before
[14]
Woods, Peter J. "Mapping Critical Anthropocene Discourses in Musical Artefacts: Whiteness, Absence, and the Intersecting “-Cenes” in Prurient’s The History of Aids." Open Cultural Studies, vol. 3, no. 1, 2019, pp. 541-553.
This limitation is existential. Protest music’s role is not just to reflect the present but to imagine a better future. It articulates alternative realities, visions of justice and equality that do not yet exist. AI, trapped in the data of the past, cannot imagine the future. It can only recombine what has already been, making it incapable of creating the kind of radical, forward-looking art that drives social change
[2]
Berger, Lawrence M. "The Emotional and Intellectual Aspects of Protest Music." Journal of Teaching in Social Work, vol. 20, no. 1, 2008, pp. 77-90.
5. The Uncanny Imitation: Why Form Without Core Fails
AI can replicate the form of protest music with remarkable precision: it can mimic the chord progressions of folk, the aggression of punk, the lyrical flow of hip-hop. It can generate lyrics that reference injustice, that use the language of resistance, that even rhyme and scan correctly. But form without core is empty. As Denisoff argues in his foundational work on protest music, "The power of protest music lies in its humanity—in the lived experience, moral conviction, and collective solidarity that cannot be coded or simulated"
[4]
Denisoff, R. Serge. Protest Songs in America. Transaction Publishers, 1968.
[4]
.
The uncanny nature of AI protest music lies in this gap: it looks and sounds like protest music, but it does not feel like it. It is a simulation that reveals its own artificiality the moment we ask: Who is speaking? What do they stand to lose? What do they hope to gain? For human protest artists, the answers are rooted in life, struggle, and love. For AI, there are no answers—only data.
This is not to say AI has no role in music. It can be a tool for human artists, a collaborator that generates ideas or streamlines production. But it can never be the source of protest music’s core. As LeVine concludes in his analysis of music and revolution, "Protest music is, at its heart, a human act—an assertion of dignity in the face of dehumanization, a cry for justice in a world of injustice"
[9]
Rabaka, Reiland. Black Power Music! Protest Songs, Message Music, and the Black Power Movement. Lexington Books, 2022.
[9]
.
6. Conclusion
Artificial Intelligence cannot replicate the human core of protest music because it cannot be human. It lacks the lived struggle that fuels resistance, the moral conviction that drives action, the collective solidarity that unites movements, and the radical originality that redefines art. AI can produce an uncanny imitation—technically flawless, stylistically accurate—but it cannot produce protest music that matters.
Protest music is not a product to be generated; it is a practice to be lived. It is the sound of humans refusing to be silent, of marginalized people claiming their voice, of a collective woowcan ever simulate. As long as there is injustice, there will be protest music—and it will always be made by humans, because only humans can carry the weight of the struggle, the hope of liberation, and the unquenchable desire for justice.
Author Contributions
Mark Howard Levine: Conceptualization, Writing – original draft, Writing – review & editing
Conflicts of Interest
The author declares no conflict of interest.
References
[1]
Barker, Thomas. "Music, Civil Rights, and Counterculture: Critical Aesthetics and Resistance in the United States, 1957-1968." Durham e-Theses, Durham University, 2016.
Denisoff, R. Serge. Protest Songs in America. Transaction Publishers, 1968.
[5]
Denisoff, R. Serge, and Mark H. Levine. "The One Dimensional Approach to Popular Music: A Research Note." Journal of Popular Culture, vol. 4, no. 4, 1971, pp. 911-918.
Reynolds, Simon. Rip It Up and Start Again: Postpunk 1978-1984. Penguin Books, 2005.
[12]
Rose, Tricia. Black Noise: Rap Music and Black Culture in Contemporary America. Wesleyan University Press, 1994.
[13]
Street, John. Music and Politics. Polity Press, 2011.
[14]
Woods, Peter J. "Mapping Critical Anthropocene Discourses in Musical Artefacts: Whiteness, Absence, and the Intersecting “-Cenes” in Prurient’s The History of Aids." Open Cultural Studies, vol. 3, no. 1, 2019, pp. 541-553.
Levine, M. H. (2026). The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music. Humanities and Social Sciences, 14(2), 150-153. https://doi.org/10.11648/j.hss.20261402.20
Levine, M. H. The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music. Humanit. Soc. Sci.2026, 14(2), 150-153. doi: 10.11648/j.hss.20261402.20
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number = {2},
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url = {https://doi.org/10.11648/j.hss.20261402.20},
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abstract = {Artificial Intelligence is no longer a subject of science fiction. It is being used in many fields to perform an infinite number of tasks. Some even use AI to create music. This paper examines the fundamental inability of artificial intelligence systems to produce authentic protest music, building on critical discussions of human subjectivity, cultural memory, and political expression in vernacular and popular music. Drawing on the work of R. Serge Denisoff and Mark H. Levine, alongside scholarship in the sociology of music and cultural theory, in addition to the author’s personal experience as a songwriter, this argument demonstrates that protest music depends on irreducibly human qualities: lived experience, moral consciousness, historical embeddedness, collective empathy, and intentional political resistance. While AI music generation tools can mimic formal and stylistic elements of protest songs—lyrical patterns, chord progressions, vocal tone, and thematic vocabulary—they cannot replicate the subjective, emotional, and social foundations that give such music meaning and transformative power. By analyzing representative protest songs across the twentieth and twenty first centuries, this paper shows how authentic protest music – human-made music -- emerges from specific historical struggles. Meanwhile AI-composed music remains a decontextualized, apolitical hollow or “uncanny imitation” This study argues that human subjectivity is not an incidental feature of political music; it is its very condition of possibility.},
year = {2026}
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AB - Artificial Intelligence is no longer a subject of science fiction. It is being used in many fields to perform an infinite number of tasks. Some even use AI to create music. This paper examines the fundamental inability of artificial intelligence systems to produce authentic protest music, building on critical discussions of human subjectivity, cultural memory, and political expression in vernacular and popular music. Drawing on the work of R. Serge Denisoff and Mark H. Levine, alongside scholarship in the sociology of music and cultural theory, in addition to the author’s personal experience as a songwriter, this argument demonstrates that protest music depends on irreducibly human qualities: lived experience, moral consciousness, historical embeddedness, collective empathy, and intentional political resistance. While AI music generation tools can mimic formal and stylistic elements of protest songs—lyrical patterns, chord progressions, vocal tone, and thematic vocabulary—they cannot replicate the subjective, emotional, and social foundations that give such music meaning and transformative power. By analyzing representative protest songs across the twentieth and twenty first centuries, this paper shows how authentic protest music – human-made music -- emerges from specific historical struggles. Meanwhile AI-composed music remains a decontextualized, apolitical hollow or “uncanny imitation” This study argues that human subjectivity is not an incidental feature of political music; it is its very condition of possibility.
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Levine, M. H. (2026). The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music. Humanities and Social Sciences, 14(2), 150-153. https://doi.org/10.11648/j.hss.20261402.20
Levine, M. H. The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music. Humanit. Soc. Sci.2026, 14(2), 150-153. doi: 10.11648/j.hss.20261402.20
@article{10.11648/j.hss.20261402.20,
author = {Mark Howard Levine},
title = {The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music},
journal = {Humanities and Social Sciences},
volume = {14},
number = {2},
pages = {150-153},
doi = {10.11648/j.hss.20261402.20},
url = {https://doi.org/10.11648/j.hss.20261402.20},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20261402.20},
abstract = {Artificial Intelligence is no longer a subject of science fiction. It is being used in many fields to perform an infinite number of tasks. Some even use AI to create music. This paper examines the fundamental inability of artificial intelligence systems to produce authentic protest music, building on critical discussions of human subjectivity, cultural memory, and political expression in vernacular and popular music. Drawing on the work of R. Serge Denisoff and Mark H. Levine, alongside scholarship in the sociology of music and cultural theory, in addition to the author’s personal experience as a songwriter, this argument demonstrates that protest music depends on irreducibly human qualities: lived experience, moral consciousness, historical embeddedness, collective empathy, and intentional political resistance. While AI music generation tools can mimic formal and stylistic elements of protest songs—lyrical patterns, chord progressions, vocal tone, and thematic vocabulary—they cannot replicate the subjective, emotional, and social foundations that give such music meaning and transformative power. By analyzing representative protest songs across the twentieth and twenty first centuries, this paper shows how authentic protest music – human-made music -- emerges from specific historical struggles. Meanwhile AI-composed music remains a decontextualized, apolitical hollow or “uncanny imitation” This study argues that human subjectivity is not an incidental feature of political music; it is its very condition of possibility.},
year = {2026}
}
TY - JOUR
T1 - The Uncanny Imitation: Why Artificial Intelligence Cannot Replicate the Human Core of Protest Music
AU - Mark Howard Levine
Y1 - 2026/04/21
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N1 - https://doi.org/10.11648/j.hss.20261402.20
DO - 10.11648/j.hss.20261402.20
T2 - Humanities and Social Sciences
JF - Humanities and Social Sciences
JO - Humanities and Social Sciences
SP - 150
EP - 153
PB - Science Publishing Group
SN - 2330-8184
UR - https://doi.org/10.11648/j.hss.20261402.20
AB - Artificial Intelligence is no longer a subject of science fiction. It is being used in many fields to perform an infinite number of tasks. Some even use AI to create music. This paper examines the fundamental inability of artificial intelligence systems to produce authentic protest music, building on critical discussions of human subjectivity, cultural memory, and political expression in vernacular and popular music. Drawing on the work of R. Serge Denisoff and Mark H. Levine, alongside scholarship in the sociology of music and cultural theory, in addition to the author’s personal experience as a songwriter, this argument demonstrates that protest music depends on irreducibly human qualities: lived experience, moral consciousness, historical embeddedness, collective empathy, and intentional political resistance. While AI music generation tools can mimic formal and stylistic elements of protest songs—lyrical patterns, chord progressions, vocal tone, and thematic vocabulary—they cannot replicate the subjective, emotional, and social foundations that give such music meaning and transformative power. By analyzing representative protest songs across the twentieth and twenty first centuries, this paper shows how authentic protest music – human-made music -- emerges from specific historical struggles. Meanwhile AI-composed music remains a decontextualized, apolitical hollow or “uncanny imitation” This study argues that human subjectivity is not an incidental feature of political music; it is its very condition of possibility.
VL - 14
IS - 2
ER -
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