Building a Future With Consent-Based AI Training
AI doesn’t need to be extractive. It doesn’t need to rely on invisible labor, unpaid creativity, or ethically murky data practices. But if we want an alternative, we have to build it — on purpose, and from the ground up.
The current model of AI development is dominated by speed and scale. Datasets are scraped without consent. Attribution is erased. Creators — whether they’re artists, journalists, poets, or coders — are left out of the loop. Their work powers the systems, but they rarely share in the value.
Consent-based AI training offers another path. It’s not just a technical fix. It’s a moral foundation — one that treats creators not as free inputs, but as stakeholders.
This isn’t about slowing down progress. It’s about choosing what kind of progress we want.
The Problem We’re Trying to Solve
Right now, most generative AI models are trained on massive datasets collected from the open internet. That includes:
Books and articles
Code and academic papers
Visual art and music
Blog posts, social media, and forums
This data is scraped at scale, often without disclosure, and almost never with permission. The result is a system built on:
No consent: Creators aren’t asked.
No credit: Their names are stripped or obscured.
No compensation: Their labor creates value for others — for free.
That’s not innovation. That’s exploitation.
What Consent-Based Training Looks Like
Consent-based AI training flips the system. It starts with permission, not assumption.
Here’s what it includes:
1. Informed Consent
Creators should be able to opt in — knowingly, clearly, and voluntarily. That means transparent policies, simple interfaces, and a real choice.
2. Transparent Datasets
We need documented datasets that show what’s included, when it was collected, under what license, and with whose permission. No more black-box training.
3. Attribution Infrastructure
When an AI model generates something based on a known style, voice, or dataset, it should say so. Even if the match is probabilistic, attribution can be approximated.
4. Revenue Sharing
If a creator’s work meaningfully contributes to a commercial model, they should share in the profits. Just like samples in music or stock images in design.
5. Right to Removal
Creators should have the option to revoke consent, especially if the use of their work changes. Permanence without transparency is not consent — it’s entrapment.
Who’s Doing This — And Who Isn’t
Some open-source and ethics-driven projects are trying to lead the way:
Spawning’s “Have I Been Trained?” lets artists check if their work is in AI datasets — and opt out.
OpenMMLab and other academic projects are experimenting with opt-in consent flows.
Some artists are forming data cooperatives to license work on fair terms.
But the major players — OpenAI, Meta, Google, Amazon — continue to rely largely on scraped data. Disclosures are minimal. Opt-outs are clunky or symbolic. And revenue sharing remains absent.
Why This Matters
Consent-based training isn’t just good ethics. It’s good infrastructure. It builds:
Trust: Users know where content came from.
Quality: Opt-in data tends to be cleaner, clearer, and more aligned.
Longevity: Models built on shaky foundations face legal and social risk. Consent builds resilience.
Diversity: Creators from underrepresented backgrounds may be more willing to contribute when they’re respected.
It’s also the right thing to do.
But Isn’t It Too Hard?
That’s the most common objection. The idea is that AI needs scale — and scale doesn’t have time for permission.
But difficulty isn’t a reason to abandon ethics. We’ve faced this argument before:
GDPR was “too hard.” It became law.
Fair trade was “too niche.” Now it’s expected.
Environmental standards were “bad for business.” Now they’re table stakes.
Yes, consent-based training is harder than scraping. But the alternative is a system that treats human expression as raw material — without dignity, without agency, without repair.
That’s not the future we want.
Imagining a Better Future
Picture this:
A creator uploads work to a shared training pool.
They tag it with permissions, styles, intended use cases.
AI developers license that data — either for free (with attribution) or for a fee.
The model cites or links back to contributors.
Revenue is shared, even fractionally, when outputs lead to profit.
Communities set cultural terms — not just tech companies.
That’s not a fantasy. It’s a design choice.
We’ve done it in music. In publishing. In photography. We can do it here too.
Conclusion: Building With Consent and Care
AI is not magic. It’s made from us — our words, our images, our code, our stories.
So the question isn’t whether AI can be trained responsibly. The question is whether we’re willing to choose a path that honors the people who make AI possible.
Consent-based AI training isn’t a constraint. It’s a foundation. One that prioritizes:
Equity over speed
Collaboration over extraction
Human dignity over convenience
This closes our creator rights series. But it opens a bigger conversation.
Let’s build the next wave of AI — not on what we can take, but on what we choose to share.
References and Resources
The following sources inform the ethical, legal, and technical guidance shared throughout The Daisy-Chain:
U.S. Copyright Office: Policy on AI and Human Authorship
Official guidance on copyright eligibility for AI-generated works.
UNESCO: AI Ethics Guidelines
Global framework for responsible and inclusive use of artificial intelligence.
Partnership on AI
Research and recommendations on fair, transparent AI development and use.
OECD AI Principles
International standards for trustworthy AI.
Stanford Center for Research on Foundation Models (CRFM)
Research on large-scale models, limitations, and safety concerns.
MIT Technology Review – AI Ethics Coverage
Accessible, well-sourced articles on AI use, bias, and real-world impact.
OpenAI’s Usage Policies and System Card (for ChatGPT & DALL·E)
Policy information for responsible AI use in consumer tools.
