The Ethical and Legal Landscape of Optimising Content for AI

The Ethical and Legal Landscape of Optimising Content for AI
June 10, 2025

It goes without saying that the intersection of content management and AI presents a complex web of ethical and legal considerations of which content creators, marketers, and platform owners must be aware. As AI driven LLMs increasingly ingest and repurpose massive amounts of digital content, a number of fundamental questions arise:

• How do current copyright laws apply when AI training datasets include third-party content?
• What liabilities might creators face if their private or gated work is scraped without explicit permission?
• What responsibilities do AI developers have to ensure transparency and respect for intellectual property rights, and how enforceable are opt-out mechanisms like robots.txt or IP restrictions?

Let’s find out.

The Reality of Opt-Out Mechanisms and Content Control

Technically, content owners often deploy opt-out tools such as robots.txt files or IP-based restrictions to discourage or block crawlers from accessing their material. However, enforcement is often difficult and inconsistent. Many AI companies claim to respect these exclusions, yet some scrapers purposely ignore them, resulting in unauthorised data harvesting. A recent lawsuit involving iFixit and Anthropic’s AI crawler “ClaudeBot” brought these issues to light and highlighted the challenges in balancing open data collection with respect for site owners’ preferences.

Implications for Monetised Content

For creators monetising their work, unauthorised inclusion in AI training sets can undercut well-crafted business models. AI-generated outputs based on proprietary content may compete with or dilute the value of original creations, raising questions about fair compensation or rights management. This is especially pressing in industries like journalism, where news outlets invest heavily in original reporting but see snippets repurposed without attribution or payment.

The Evolving Copyright Landscape

It is becoming increasingly evident that copyright law is struggling to keep pace with AI’s rapid development. Several high-profile lawsuits have been filed against AI companies, challenging the legality of scraping copyrighted works without explicit licenses.

The New York Times, along with other major publishers and authors, filed a class-action lawsuit against OpenAI in 2023, alleging unlawful use of their copyrighted content in training datasets. This case, as well as many others, is setting important precedents about AI training data rights and creator protections.

The Opportunity and the Grey Areas

The opportunity to have your content referenced by LLMs is huge: amplified visibility, long-tail traffic, and thought leadership to name but a few. But unlike SEO, where rules are codified, LLM optimisation plays out in a legal and ethical frontier that is still being defined. What does it mean for your content to be scraped and reused? Where does the line between fair use and infringement lie?

Copyright Considerations

LLMs are typically trained on content that is:

• Publicly available
• Licensed
• In the public domain

However, many authors may find their content used without clear attribution or consent. While LLMs don’t store data verbatim, the reuse of patterns and summarised knowledge may still raise questions about ownership and proper licensing.

Licensing Content for AI Use

Using permissive licenses like Creative Commons (e.g., CC BY) explicitly signals that your content can be reused, referenced, and adapted. This removes ambiguity and makes it legally easier for AI models to absorb and use your work.

Platform Terms of Service and Data Use

Where you post content matters. Platforms like Medium, Substack, or GitHub may have terms that either permit or restrict how content is scraped and reused. Read the fine print. Just because your work is public doesn’t mean it’s legally open-source.

AI Transparency and Consent

A growing demand is surfacing for AI transparency: to disclose what data was used to train models and how that data was obtained. This affects not only content creators but also the platforms and developers behind LLMs. Voluntary disclosure, citation practices, and opt-in/opt-out mechanisms are being debated, and could soon become standard.

Risks of Misrepresentation, Misinformation, and Brand Dilution

When AI paraphrases your content, it may lose nuance or accuracy. Worse still, content may be associated with contexts you don’t endorse, leading to brand risk or reputational harm. Ethical content strategy therefore must account for how AI might repurpose your material.

Critical Questions Moving Forward

As the legal and ethical debates evolve, it is important that content creators, managers and industry stakeholders consider several pressing concerns:

• How do we apply existing copyright frameworks when AI systems ingest massive amounts of third-party data?
• What are the liabilities if private content is used without consent?
• What level of transparency and accountability can AI developers maintain when choosing data sources?
• Are the current opt-out mechanisms sufficient in protecting creator rights or do we need new legislation?
• How will regulations shape the balance between AI generated content and human authorship?

Recent Regulatory Proposals and Industry Best Practices

In response to these challenges, regulators across the globe are developing AI-specific policies which aim to balance innovation with intellectual property protections:

• The European Union’s AI Act mandates transparency, data source disclosures, and respect for copyright and privacy, setting a global precedent for responsible AI development.
• The US Copyright Office recommends clearer licensing frameworks to address AI training datasets, emphasising a balance between fair use and creator rights.
• The UK Intellectual Property Office is consulting on reforms to clarify ownership of AI-generated content and lawful dataset usage.

At the same time, AI developers and content creators continue to adopt best practices to foster compliance and trust, including:

• Increasing transparency via data auditing and disclosure of sources.
• Developing opt-in licensing models that compensate creators for dataset inclusion.
• Rigorously respecting opt-out signals like robots.txt.
• Collaborating with creators to co-create datasets which align commercial and IP interests.

The Role of Open Data Projects

Projects like Common Crawl and LAION aggregate public web data for use in LLM training. Participating in or supporting these initiatives offers a way to make your content accessible while aligning with the open-source and ethical AI communities.

Future Outlook: Ethics-by-Design in Content Strategy

Rather than waiting for the law to catch up, content creators can adopt an ethics-by-design approach:

• Be transparent about licensing
• Design for safe reuse (e.g., clear structure, disclaimers)
• Monitor AI outputs for misuse or misinterpretation

Ethical visibility is possible. It’s not just about being seen, it’s about being seen right.

Final Thoughts

Although we are still in the early stages, emerging regulations and evolving industry practices are laying the foundation for a future where AI-driven content generation can coexist with strong protections for creator rights.

Such a dual focus on innovation and responsibility is essential. Optimising for LLMs means walking a fine line between opportunity and ethical obligation.

By embracing open licensing, respecting platform terms, and staying informed on global regulation, you not only safeguard your work, you actively contribute to shaping the standards of tomorrow. In the age of AI, the way your content is used matters just as much as what it says. Make it count.At Take3, we help creators navigate the complex intersection of AI, copyright, and content strategy. If you’re ready to optimise your work for LLM visibility, ethically and sustainably, book a call with us today. Let’s future-proof your content the right way.