European Union AI act: transparency over enforcement

Written by Jorge Pascual - Corporate Lawyer at Techsoulogy
Created Sep 26, 2023 | 4 min read
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Everything you need to know about AI European Regulation (so far) 

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The European Union’s (EU) landmark AI Act, recently approved by the European Parliament, is poised to reshape the AI landscape within its borders. In this comprehensive analysis, that follows our last article tackling the AI Act draft by the European Commission, we delve into the critical facets of this text, examining how it addresses the principal aspects regarding AI: data sources, governance, copyrighted data, compute resources, energy efficiency, capabilities, limitations, risk mitigation, evaluations, testing, machine-generated content, the roles of EU-member states, and downstream documentation. This exploration aims to provide an understanding of the EU’s strategic approach to AI regulation based on key aspects of the development of such technologies. 

Data sources and governance: Promoting transparency and accountability

The text passed by the European Union Parliament recognizes that AI’s foundation lies in data. To foster trust and accountability, AI systems are now required to explicitly disclose their data sources and the nature of the data they employ. This transparency measure ensures users have clear insights into the data underpinning AI decision-making processes.

In practice, this means that AI developers across Europe must be meticulous in documenting their data sources. Consider a company operating an AI-driven advertising platform within the EU. It will be mandated, under the recently passed act, to fully disclose the origins of its data and the categories used for ad targeting. In practice, the company operating the platform shall be crystal clear on how it sources data from a network of publishers across the European Union or applicable territory, and that this data encompasses information such as user demographics, browsing behavior, and ad engagement metrics. This transparency not only strengthens user trust but also bolsters accountability, a core tenet of the European Union’s AI strategy. 


Everything you need to know about AI European Regulation (so far)

Copyrighted data and AI: Striking a balance

One of the notable challenges in AI development is navigating the intersection of copyrighted data and AI algorithms. The EU’s regulation emphasizes the importance of respecting intellectual property rights while allowing for “fair use”. Developers must tread carefully when incorporating copyrighted content into AI models, securing necessary permissions and licenses, and ensuring proper attribution. The biggest challenge underlies the enforcement of such intellectual property rights. It is still a mystery how will the European Union translate this mandate into a strategy to grant proper protection to intellectual property rights.

Now, imagine a firm that utilizes copyrighted images in its AI-generated art platform. To comply with the EU’s act, it must navigate the intricate web of copyright laws by obtaining proper licensing and ensuring accurate attribution. Again, transparency is set as the “golden standard” when developing AI in the European Union.

Compute resources and energy efficiency: Tackling environmental concerns

AI’s computational demands have been a subject of environmental concern. For example, deep learning neural networks, fundamental to AI, require substantial computational power. Acknowledging the environmental impact of energy-intensive AI model training, the recently passed act encourages the development of energy-efficient algorithms and hardware solutions.

For example, NVIDIA’s CUDA platform optimizes Graphical Processing Units (GPU) usage for AI applications, significantly reducing energy consumption. By reducing the carbon footprint associated with AI model training, this kind of tools aligns its work with both AI innovation and broader sustainability objectives.

Capabilities and limitations: Categorizing risk

The EU’s act introduces a risk-based categorization of AI applications. High-risk applications, such as AI in healthcare diagnostics or autonomous vehicles, face more rigorous requirements than generative AI tools. Developers must ensure these applications adhere to stringent safety and transparency standards to prevent harm to individuals or society.

The EU considers healthcare diagnostic apps with AI and autonomous vehicles to be high risk.

Consider an automotive manufacturer deploying AI in autonomous vehicles. To meet EU safety standards, the company subjects its AI-driven systems to exhaustive testing, ensuring they comply with regulatory requirements as it is considered a high-level risk model. This categorization introduces a proportionality treatment that aims at moderating the risks of “speeding” the development of artificial intelligence tools.

Risk mitigation: Proactive safety measures

Proactive and preemptive risk mitigation is at the heart of the EU’s AI act. Developers are now required to conduct comprehensive risk assessments, identify potential harms, and implement strategies to mitigate them. This approach safeguards against AI systems causing harm, including bias, discrimination, or other adverse effects associated with AI models. Proper safety measures shall be introduced in the development plan of every AI model, assuring a reliable analysis of the possible outcomes of the technology.

In fact, there are tools such as IBM’s AI Fairness 360 toolkit that detect and mitigate bias in machine learning models, reducing the risk of discriminatory outcomes. This will allow for better adjustment of AI models throughout their “life” to avoid undesired outcomes, such as aggravating “human tendencies”.

Evaluations and testing: Ensuring reliability

The EU’s act places a strong emphasis on thorough evaluations and testing of AI systems, both before and after market entry. Rigorous testing verifies that AI systems meet safety and performance criteria, instilling confidence in users and regulators alike.

Imagine an AI developer specializing in medical diagnosis software. Before introducing its system to the market, the company subjects it to exhaustive testing to ensure compliance with EU healthcare regulations, guaranteeing patient safety. On the other hand, the level of testing required for simpler AI tools, such as basic conversational AI, will be less demanding compared to medical diagnosis technology.


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Machine-generated content: Ensuring transparency

AI-generated content has become prevalent across various applications, from automated news articles to art generation. The EU’s act provides clear guidelines for developers regarding machine-generated content. Transparency is, again, paramount: when AI generates content, it must be clearly indicated as such to prevent consumer confusion.

Consider a media company deploying AI for news reporting. By conspicuously labeling AI-generated content, the company complies with EU transparency requirements. This approach ensures that readers are aware when content is generated by AI, maintaining transparency and trust.

Roles of member states: Balancing unity and flexibility

While the act provides a unified framework, it respects the roles of individual member states. Member states retain the flexibility to adapt certain aspects to local needs, ensuring a consistent EU-wide framework while allowing for nuanced implementation. In fact, each member state will create surveillance authorities in charge of law enforcement.

An illustrative example could be Techsoulogy’s case. When developing our Artificial Intelligence models, we will need to align our AI ethics framework with both EU regulations and Spanish privacy laws. This dual compliance approach acknowledges the specificities of national and EU-level legislation, promoting responsible AI development and permitting member-states to tackle specific needs in each territory.

Downstream documentation: Enhancing accountability ex-post

Developers are now mandated to maintain comprehensive documentation outlining AI system development, deployment, and usage. This documentation serves as a vital resource for investigations or audits in the event of issues or disputes. It ensures transparency, accountability, and compliance throughout the AI lifecycle.


The European Union’s Artificial Intelligence Act represents a monumental step in AI governance. By addressing the topics pointed out above, among others, it constructs a comprehensive AI framework for sustainable AI development. This framework fosters innovation while emphasizing ethical and responsible AI practices, safeguarding individuals and society. 

In a world increasingly shaped by AI, the EU’s commitment to establishing a trustworthy AI ecosystem sets a global example. This regulatory approach aims at AI serving humanity’s best interests while adhering to ethical standards and legal responsibilities. While the possibilities to enforce such framework remains unclear, the European Union takes a step towards a bigger goal, it’s not merely about regulating technology; it’s about forging a future where AI enriches lives while respecting rights and dignity.

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