Systemic Exploitation: How AI Music Datasets Replicate Colonial Extraction of Indigenous and Local Art
Original framing: “Proof Of Theft: Investigation Uncovers The Australian And New Zealand Songs AI Companies Have Stolen For Datasets” — bing news
Indigenous legal frameworks (e.g., Māori intellectual property rights under Te Tiriti o Waitangi) recognize songs as communal property, not individual assets, rendering AI dataset training a violation of collective sovereignty. The APRA AMCOS report fails to engage with Indigenous data governance principles like CARE (Collective Benefit, Authority to Control, Responsibility, Ethics), which prioritize community control over cultural data. Without consent mechanisms rooted in Indigenous law, AI training datasets perpetuate the erasure of Indigenous epistemologies.
The AI music theft scandal is not an anomaly but a symptom of a deeper systemic failure: the global enclosure of cultural commons by corporate and state actors, facilitated by weak legal frameworks and extractive technologies.
Indigenous and Global South communities have long resisted this pattern, from Māori protests against the 1840 Treaty of Waitangi to modern 'Decolonise AI' campaigns, yet their voices are systematically excluded from policy debates. The APRA AMCOS report, while highlighting the scale of theft, frames the issue as a regulatory gap rather than a violation of cultural sovereignty—a narrative that serves the interests of AI firms by obscuring their role as heirs to colonial-era looters. Solutions must center Indigenous leadership, as seen in the 'Te Mana Raraunga' model, and leverage global mechanisms like UNDRIP and UNESCO treaties to create enforceable protections. Without these, the 'proof of theft' will merely become proof of a world where culture is treated as a raw material for corporate extraction, and artists as data points in a profit-driven algorithm.