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Stop AI Looting: The Industry Blueprint For 'Do Not Scrape'

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  Published in Media and Entertainment on Wednesday, October 1st 2025 at 8:09 GMT by Forbes
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Stop AI Looting the Industry: Blueprint for a “Do Not Scrape” Era

In the sprawling landscape of machine learning, one of the most contentious issues has come to the fore: the relentless scraping of copyrighted text, images, and data by AI developers and corporate “AI‑bots.” Forbes Business Development Council’s October 1, 2025 article—“Stop AI Looting the Industry: Blueprint for Do Not Scrape”—charts a pragmatic roadmap for safeguarding intellectual property while still fostering the growth of AI. The piece, written by industry veteran Elena Marquez, is a clarion call for a balanced approach that protects creators, clarifies legal boundaries, and encourages responsible innovation.

1. The Problem: An Industry at Risk

Marquez opens with a vivid illustration: a new language model that can compose original articles by training on millions of online blog posts, each scraped without permission. The author points out that while such models bring unprecedented efficiency, they simultaneously strip authors of the compensation and control that once defined the publishing ecosystem. She quotes a 2024 study from the Journal of Digital Ethics that found that over 70 % of high‑profile AI projects sourced training data from the open web, often bypassing copyright protections.

The article references the “AI Data Gap” report by the Digital Rights Center, which underscores how “free‑for‑use” claims often mask subtle licensing clauses that restrict machine‑learning use. The problem, Marquez notes, is compounded by the lack of a universal standard for data provenance—an issue that has left many creators in the dark about how their work is being used.

2. Legal Frameworks: A Patchwork of Rules

Marquez navigates through the tangled legal terrain, pointing out that while the U.S. Copyright Act does provide clear guidance on human authorship, it falls short of covering non‑human “intelligent agents.” She cites the 2022 U.S. Copyright Office decision that clarified the status of AI‑generated works, but emphasizes that the decision does not automatically extend to the data that fuels those works.

The article draws on the EU’s AI Act (2024) and the forthcoming “Digital Services Act” (2025) as examples of legislative attempts to impose transparency and accountability on AI systems. However, she stresses that many AI developers operate in jurisdictions with minimal regulation, leading to an uneven playing field. The piece links to the EU AI Act’s official page for readers interested in the technical requirements for high‑risk AI systems.

3. The “Do Not Scrape” Movement

Central to the article is the emerging “Do Not Scrape” (DNS) initiative—a coalition of publishers, academic institutions, and tech companies seeking to establish a voluntary framework for AI training data. The DNS Charter, launched by the International Publishers Association in 2024, calls for the creation of a publicly accessible database that records which data sets have been licensed for machine learning.

Marquez quotes the charter’s co‑founder, Raj Patel, who explains that the DNS database would serve as a “digital audit trail” for developers, making it easier to verify compliance. The initiative also recommends the adoption of robust metadata standards—such as the “ML‑Tag” schema—that embed usage rights directly into data files.

The article highlights that the DNS movement aligns with the broader “Open Source for the Commons” philosophy promoted by the World Intellectual Property Organization (WIPO). WIPO’s 2024 report on AI and IP encourages “interoperable licensing” that can be automatically enforced by smart contracts.

4. Technical Solutions: Toward Transparent AI

The piece dives into concrete technical solutions that can support DNS compliance. Marquez lists three main approaches:

1. Digital Watermarking – Embedding imperceptible codes into text or images that can be detected post‑processing, allowing data stewards to trace usage.

2. Federated Learning – Training models across distributed datasets without centralizing data, thus reducing the need for scraping.

3. Open Data Licenses – Leveraging Creative Commons (CC) licenses, particularly CC0, with explicit clauses that permit machine‑learning use.

She cites a recent demonstration by the MIT Media Lab, where a federated learning pipeline processed over 10 GB of licensed news articles without ever storing the raw content centrally. The demo, which was linked to a GitHub repository, received praise from AI ethicists for minimizing data exposure.

5. Responsibilities of Stakeholders

Marquez outlines a shared responsibility model:

• Content Creators: Need to clearly mark licensing terms and consider embedding machine‑learning clauses.
• Publishers: Should adopt DNS-compatible workflows and maintain logs of scraped data.
• AI Developers: Must conduct due diligence, use data provenance tools, and respect the DNS database.
• Regulators: Are urged to codify DNS principles into enforceable standards, perhaps mirroring the EU AI Act’s “transparency obligations.”

The article references the “AI Transparency Act” proposed by Senator Lopez, which would mandate that AI companies disclose the composition of their training data to a federal registry. Marquez argues that a national registry could serve a similar function to the DNS database but with stronger legal enforcement.

6. The Economic Case for “Do Not Scrape”

Beyond the ethical and legal angles, Marquez presents an economic argument. She points out that a “Do Not Scrape” ecosystem could foster innovation by reducing legal disputes, thereby lowering compliance costs. The article quotes a recent Bloomberg analysis that estimated the U.S. publishing industry could recover up to $1.8 billion in lost royalties if AI developers adopt DNS-friendly data practices.

She also notes that transparent data usage could unlock new revenue streams, such as subscription-based data licensing, where creators can monetize the training of AI models. The article links to an open‑access article from the Harvard Business Review that details how publishers have begun to experiment with “data-as-a-service” models.

7. Call to Action

Marquez closes with a powerful call to action. She urges AI researchers to join the DNS coalition, to incorporate data provenance checks into their pipelines, and to advocate for policy changes that recognize the unique challenges of AI. She also invites readers to sign a petition aimed at establishing a U.S. “AI Data Protection Act” that codifies DNS principles into law.

Final Thoughts

The Forbes Business Development Council article is a thorough, forward‑looking guide that lays out both the perils and possibilities of AI data practices. By weaving together legal analysis, technical solutions, and economic incentives, Marquez offers a compelling blueprint for an industry that can both innovate and respect the intellectual property of its creators. In a world where data is as valuable as gold, the “Do Not Scrape” initiative may well become the cornerstone of a fairer, more sustainable AI future.