The music industry is sprinting towards an AI-powered future — but from both a technical and practical viewpoint, the term “ethical” label isn’t the true safety net that it’s hyped up to be. Without real accountability, creators, labels, and publishers risk losing the long-term value of their work to opaque systems and rushed standards. AI presents incredible opportunities, but only if we confront its challenges head-on.
This isn’t about vilifying AI companies or picking sides. Every AI company is biased, whether they want to be or not. Many developers have good intentions. But when it comes to attribution — determining how much creators influence AI outputs — good intentions aren’t enough.
The Mirage of Ethical AI
“Ethical AI” sounds great: systems trained on licensed, public domain, or owned content. But it’s not enough. Even ethically trained systems crumble without robust attribution frameworks. Tossing a little money at creators is just a Band-Aid, not a solution.
Proportional attribution — quantifying how much a creator’s work shapes AI outputs — is the only way to ensure fairness. Think of it like this: handing someone loose change after they’ve designed and built your home isn’t recognition; it’s an insult. True attribution values the creator’s full impact and ensures everyone — artists, songwriters, labels, and publishers — gets their fair share.
Lessons from the Visual World
Visual artists have already seen what happens when attribution is an afterthought. Their unique creations were scraped, repurposed, and turned into raw material for AI systems — with no credit, compensation, or control. One-time, flat fees became their consolation prize as the technology evolved to no longer need them.
The result? Many were erased from their own creative ecosystems. The music industry is at risk of repeating this mistake. Without proper attribution systems, musicians and, in particular, songwriters could be reduced to invisible scaffolding, fueling AI’s rise while losing their seat at the table.
The Self-Regulation Trap
Here’s the problem: AI companies often try to handle attribution themselves. They create the rules, measure influence, and decide payouts — all while profiting from the results. It’s like asking the fox to guard the henhouse. Even with the best intentions, bias is inevitable.
Self-regulated attribution also leads to chaos. If every AI company uses different systems and metrics, how will labels, publishers, and creators advocate for fairness? How will creators prove their contributions? Collaboration requires consistency, and consistency demands independent, industry-wide standards.
Attribution Is a Beast
Attribution is extremely difficult — far more than most people realize. Simplistic methods like genre tagging, metadata matching, or embedding similarity don’t just fail; they actively distort how creative influence is measured. Here’s why they fall short:
Cross-Genre Influence Is Ignored: Imagine a jazz riff inspiring a chart-topping pop hit or a classical progression shaping a viral hip-hop beat. Basic methods fail to recognize these nuanced connections. Influence isn’t confined by genre — it’s dynamic, fluid, and deeply interwoven.
The “Homogeneous Dataset” Problem: Picture a hip-hop track dominating streaming charts but lumped into an AI model alongside thousands of other hip-hop songs. It might barely register as influential without nuanced attribution — even if its emotional resonance shaped the genre. Basic methods reward quantity over quality, erasing the true impact of standout works.
Publishing vs. Recording Rights Get Blurred: Attribution systems often fail to distinguish between who wrote a song and who performed it. This distinction directly affects how creators and rights holders are compensated, yet many methods lump these contributions together.
Nuanced Influence Is Lost: Influence isn’t binary. A song’s emotional weight, cultural impact, and personal connection to listeners don’t fit neatly into algorithms. AI systems treat creative works as raw data, stripping away the human meaning behind the numbers.
Simplistic or self-regulated attribution doesn’t just make mistakes — it fundamentally erodes trust. Creators, labels, and publishers will all end up fighting over mismatched metrics, each holding a different version of the truth.
Attribution Can Be Mastered
Here’s the good news: accurate attribution isn’t impossible — it’s just hard. With the right tools and expertise, it can be made simple, reliable, and transparent.
We at Sureel, have spent over two years developing patented attribution technology designed to tackle this exact challenge. Its approach combines explainable AI (XAI) with mathematical precision and importantly human perception studies to create results that align with both logic and creativity.
Accurate attribution isn’t about finding shortcuts or obviously similar things — it’s about doing the hard work to capture the depth, complexity, and the value of human creative influence. With the right neutral system(s), the industry can finally move past mismatched data and inconsistent payouts.
Why Accountability Is Non-Negotiable
“Ethical AI” isn’t enough without accountability. True responsibility means systems that measure influence fairly, verified by independent parties to eliminate bias. Anything less is at best guesswork or, at its worst, corruption, and the stakes are too high to gamble with trust.
The Clock Is Ticking
AI models improve every day. If we wait until they’re producing hits without fresh input, creators lose their leverage. As competition intensifies, even well-meaning companies might cut corners under pressure.
The solution isn’t to stop AI innovation — it’s to lead and guide it with strong attribution frameworks. Attribution isn’t a luxury; it’s the foundation of a sustainable, fair music ecosystem.
A Call to Action: Stop Guessing, Start Collaborating
The future of AI generated music must not remain a guessing game, especially if there is a $42 billion dollar prize being played for. The only way forward is with clarity, consistency, and collaboration — built on independent, transparent attribution systems. Here’s what needs to happen:
Rights Holders (Labels, Publishers): Don’t fall for or settle for vague promises of self-regulation or market share dynamics. We don’t live in that world any longer. Insist on nothing less than neutral attribution frameworks that provide measurable, transparent results.
AI Companies: Don’t do it alone. Attribution isn’t your core strength — and it doesn’t need to be. Focus on building groundbreaking tools while collaborating with attribution specialists to create trust and fairness for all stakeholders.
Creators (Artists, Songwriters): Your voice matters. Demand systems that accurately represent your influence — not just as data points but as the lifeblood of creativity. Speak up and shape the standards that will define your legacy.
Coming in Part II: Why AI Companies Shouldn’t Handle Attribution
Attribution isn’t just a technical problem — it’s a full-time job requiring domain expertise, precision, and constant evolution. AI companies already face immense pressure to innovate and compete. Adding attribution to their workload risks inefficiency, inconsistency, and backlash.
In Part II, we’ll explore why leaving attribution to independent experts is the smartest move — for AI companies, creators, and rights holders alike.
The music industry is writing the first chapter of its AI story. Let’s make sure it’s not a tragedy.
