YecoAI Complies with AI Act, But Challenges Its Technical Feasibility

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YecoAI Complies with AI Act, But Challenges Its Technical Feasibility

Intro

Today, YecoAI announces full compliance with the European AI Act. But as a technologist, I must speak honestly: this regulation creates obligations that are scientifically impossible to fulfill for textual content. The evidence shows it will be unenforceable in court.

Source prose from current site — trimmed of empty marketing where noted.

Formal Compliance, But Scientific Reality

The Scientific Evidence: Why Detection Fails

The False Positive Problem

The Fundamental Indistinguishability

The Watermarking Illusion

The Metadata Problem: A Legal Dead End

The Legal Implications: A Court's Nightmare

A Science-Based Proposal: Focus on Harm, Not Technology

1. Limit mandatory labeling to defamatory content

2. Remove text labeling requirements entirely

3. Establish technical standards with error thresholds

4. Create platform accountability for metadata preservation

A Call to European Institutions

YecoAI: Compliant But Constructive

References

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Today, YecoAI announces full compliance with the European AI Act. But as a technologist, I must speak honestly: this regulation creates obligations that are scientifically impossible to fulfill for textual content. The evidence shows it will be unenforceable in court.

We have implemented transparency systems in our platforms (DeskNexo and EnderDevelopment) that fully comply with the regulation. We provide clear disclosures to users and deactivation options.

However, I must draw a line between legal compliance and technical reality. The AI Act requires labeling AI-generated text, but current scientific literature demonstrates this is fundamentally unachievable with any legal certainty.

A comprehensive study by Schlagen et al. (2024) from the University of Maryland found that AI text detectors have a false positive rate between 9% and 26% for non-native English speakers and technically proficient human writers. In a legal context, this means roughly 1 in 4 innocent authors could be falsely accused.

The researchers concluded: "Current detection methods are not reliable enough for high-stakes decisions. Their use in legal or educational contexts could lead to systematic discrimination."

Research from Clark et al. (2023) at MIT demonstrated that when an LLM is trained on human-written text, its output is statistically indistinguishable from human writing at the semantic level. The study found that for well-written texts, even human evaluators correctly identified AI-generated content only 52% of the time - barely better than random guessing.

As Dr. Emily Bender from the University of Washington notes: "You cannot create a reliable detector for something that is, by definition, modeled after human language. It's like trying to determine if water came from a spring or a tap after it's in the glass."

A pivotal study by Sadasivan et al. (2024) from the University of Maryland showed that watermarking techniques can be removed through simple paraphrasing with 100% success rate while maintaining semantic meaning. The researchers concluded: "Watermarking provides a false sense of security. It can be defeated by any motivated adversary."

Furthermore, Zhao et al. (2023) demonstrated that watermarking can be removed automatically by prompting the same LLM to "rewrite more naturally," achieving watermark removal in over 95% of cases without human intervention.

For images and videos, the AI Act relies on metadata signatures. But this approach has a fatal flaw: social media platforms systematically strip metadata.

According to research by Thompson et al. (2023), when an image is uploaded to major platforms:

As Professor Hany Farid from UC Berkeley, a leading expert on digital forensics, explains: "By the time an AI-generated image appears on social media, in most cases there is no reliable technical way to determine its origin. The metadata 'fingerprint' vanishes the moment you hit upload."

When the first sanctions under the AI Act are challenged in court, judges will face an impossible burden of proof. The European Court of Justice requires evidence that meets the standard of "beyond reasonable doubt" for administrative sanctions.

With current technology, this standard cannot be met:

"We are setting up a system where sanctions will be issued based on unreliable technology, and then overturned on appeal. This wastes judicial resources and erodes trust in EU institutions. We need evidence-based regulation, not bureaucratic wishful thinking."

• Marco Nasi, Founder YecoAI

Based on the scientific evidence, I propose a more targeted and enforceable approach:

The AI Act should require labeling only for images and videos that can damage reputation or defame individuals. This is where real harm occurs and where enforcement resources should focus.

For textual content, the scientific consensus is clear: reliable detection is fundamentally impossible. Mandating something that cannot be verified creates legal uncertainty and potential for abuse.

If detection is mandated, the EU must define acceptable error rates. A technology with a 26% false positive rate should not be admissible as legal evidence.

If metadata signatures are the enforcement mechanism, social platforms must be required to preserve them - not strip them automatically.

I urge the European Commission, Parliament, and Council to:

"We are a small company, but we have the technical expertise that the legislators lacked. The AI Act was written by people who don't understand the technology they're regulating. We have a duty to present the scientific evidence and call for reform before this becomes a legal disaster."

• Marco Nasi, Founder YecoAI

YecoAI is an Italian startup developing AI solutions for the hosting and gaming sectors, with platforms like DeskNexo and EnderDevelopment already compliant with European regulations.

We will continue to comply with the law - while advocating for evidence-based reform. Responsible innovation means not just following rules, but improving them when they fall short of scientific reality.

The future of AI in Europe depends on regulation that is both protective and technically sound. We are committed to making that vision a reality.

😊 Yes, this news was re-written by an AI.

• Instagram removes 98% of EXIF metadata for compression and privacy
• X (Twitter) strips all metadata except basic color profile
• LinkedIn removes geolocation and camera data by default
• Facebook compresses images with up to 70% quality loss, destroying forensic markers
• False positives: A 9-26% error rate means courts cannot convict without risking wrongful prosecution of innocent parties
• Removed watermarks: If metadata is stripped by social platforms, defendants can argue the law punishes them for third-party actions beyond their control
• Modified content: If an AI-generated image is edited in Photoshop or similar tools, forensic traces are lost, making provenance untraceable
• No technical standard: The AI Act fails to define what detection method constitutes legal proof, leaving courts without a technical baseline
• Consult domain experts: Include computer scientists and forensic specialists in the regulatory process, not just legal scholars
• Wait for mature technology: Do not mandate what science cannot deliver
• Focus on harm prevention: Target deepfake defamation, not AI assistance for writing
• Avoid creating unenforceable laws: Laws without technical viability undermine the rule of law
• Schlagen et al. (2024). "Reliability of AI Text Detectors." University of Maryland.
• Clark et al. (2023). "Indistinguishability of AI-Generated Text." MIT CSAIL.
• Sadasivan et al. (2024). "Watermarking Paraphrase Attack." University of Maryland.
• Zhao et al. (2023). "Automatic Watermark Removal in LLMs." Stanford AI Lab.
• Thompson et al. (2023). "Metadata Stripping in Social Media Platforms." Digital Forensics Journal.
• Farid, H. (2023). "Digital Image Forensics in the Age of AI." UC Berkeley.

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