What the EU AI Act means for AI software development: everything you need to know

AI isn’t just a tool anymore. 

AI models are making critical decisions that have a huge impact on people’s careers, finances, and lives.

But without oversight, they’re (too) often biased and unaccountable.

The EU AI Act changes that. It introduces strict rules for high-risk AI, mandating transparency, fairness, and human oversight.

But, how exactly do these new rules impact AI software development? And how do you stay compliant?

Here, we’ll break down what the law means for AI software development in the EU and give you tips on how to ensure compliance.

Let’s dive in!

What is the EU AI Act?

The EU AI Act is the world’s first comprehensive AI regulation.

Its goal is ensuring AI systems are safe, transparent, and aligned with fundamental rights. 

The EU AI Act affects anyone that develops, deploys, or sells AI systems in the EU – even if they’re not headquartered in Europe. This directly impacts:

• AI developers and providers – Companies that build AI-powered software or offer AI services.
• AI deployers – Businesses that integrate AI into their operations (e.g., banks using AI for credit scoring, hospitals using AI for diagnostics).

The law follows a risk-based approach, with stricter rules on AI applications that are a greater risk to individuals and society.

Even if you’re using third-party AI models, e.g. OpenAI APIs, you need to comply with the law if they’re used in high-risk areas like hiring, finance, or healthcare.

Or, in other words – the higher the risk, the stricter the rules.

Here’s what Ursula von der Leyen, President of the European Commission, said when the law was passed:

Today’s agreement focuses regulation on identifiable risks, provides legal certainty and opens the way for innovation in trustworthy AI. By guaranteeing the safety and fundamental rights of people and businesses, the Act will support the human-centric, transparent and responsible development, deployment and take-up of AI in the EU.

Ursula von der Leyen, President of the European Commission

The EU AI act is a structured legal framework designed to promote AI innovation while also protecting users.

But, why was the law passed, anyway?

The almost exponential growth of AI, while coming with a lot of benefits, has also exposed some deep flaws with AI systems – built-in bias, misinformation, and a lack of accountability.

Some high-profile examples include:

• Amazon’s AI hiring tool – Amazon scrapped a recruiting tool after it showed bias against female candidates.
• Dutch child care benefits scandal – A self-learning algorithm used by the Dutch tax authority falsely accused 26,000 families of committing fraud.
• Biased predictive policing tools – Predictive policing tools, trained on racially biased arrest data, reinforced existing biases in policing.
• Deepfakes – AI-generated deepfakes are being used in election manipulation, financial scams, and fake news.

And that’s just the tip of the iceberg.

The EU AI Act directly addresses these risks by requiring better transparency, human oversight, and bias prevention in AI systems.

The Act introduces severe fines for violations:

• Up to €35 million or 7% of global annual revenue for the most serious breaches.
• Up to €15 million or 3% of global turnover for failing to meet high-risk AI obligations.
• Up to €7.5 million or 1.5% of turnover for transparency failures in limited-risk AI.

The EU’s message is clear: AI must be transparent, accountable, and human-centered.

And if you want access to the EU market, you need to follow these rules.

The EU AI Act’s risk classification system

The EU AI Act introduces a risk-based approach to AI regulation. 

The higher the potential negative impact of an AI system, the more compliance requirements it has to meet.

This way, low-risk AI can be developed freely, while high-risk AI has to comply with stricter rules to protect users. 

This classification system is at the core of the EU AI Act.

EU AI Act risk levels: overview

Most business AI applications fall into the limited or high-risk categories.

If your AI system is classified as high-risk, you have to meet strict compliance requirements before you can deploy it.

High-risk AI systems are those that can significantly impact people’s rights or safety. They need to have:

• Extensive documentation – High-risk AI systems need to have detailed technical information before 
• Human oversight – Decisions made by the AI have to be reviewable and can’t be fully automated.
• Bias prevention – High-risk AI systems must be trained on fair and representative datasets to prevent biases.
• Robust security – AI systems must meet strict cybersecurity and failure prevention standards.

But, some AI applications are completely prohibited. The Act completely bans AI systems that threaten fundamental rights, safety, or democracy.

Examples include:

• Social scoring systems – AI that ranks people based on behavior, like China’s social credit system.
• Real-time facial recognition in public places – Except for specific law enforcement use cases.
• Predictive policing AI – Systems that claim to predict future crimes based on profiling

If you’re developing AI software, you should classify it early to avoid compliance issues later.

A good tip is if your AI system automates critical decisions, you should assume it’s high-risk and prepare accordingly.

You need to take this classification seriously.

Getting it wrong could mean regulatory delays, your product getting banned, or multimillion-euro fines.

And that’s not just a legal risk – it could shut down your business.

How the EU AI Act impacts AI software development

Next, we’ll break down the key ways the EU AI Act will change AI software development in Europe.

Transparency is now a legal requirement

Transparency is a core principle of the EU AI Act.

The Act mandates transparency at multiple levels, from AI-generated content to high-risk decision-making systems.

Any AI software or system must be understandable, accountable, and clearly disclosed to users.

The rise of deepfakes and AI-generated media has led to an explosion of misinformation, which is why the EU AI Act requires companies to disclose AI-generated content.

Providers of AI systems, including general-purpose AI systems, generating synthetic audio, image, video or text content, shall ensure that the outputs of the AI system are marked in a machine-readable format and detectable as artificially generated or manipulated.

Deployers of an AI system that generates or manipulates image, audio or video content constituting a deep fake, shall disclose that the content has been artificially generated or manipulated.

EU AI Act, Article 50

If your product generates AI-created content, you’ll need to implement watermarking, metadata tagging, or a clear label so users know it’s AI-made.

Also, the EU AI act bans misleading AI interactions – users always need to know they’re communicating with an AI system.

You should include a disclaimer at the start of conversations, e.g. “I’m an AI-powered assistant. Let me know if you’d like to speak with a human.”

This applies to chatbots, virtual assistants, and other limited risk AI applications, not just high-risk systems.

But, for high-risk AI, transparency isn’t just for users – it’s for regulators, too. 

If you’re building a high-risk AI product, you’ll need to document:

• How your AI models were designed and trained(including data sources)
• How your AI makes decisions (explainability reports)
• How you monitor for errors and biases

You’ll need to maintain detailed records for audits to prove compliance with the Act.

Data handling and bias mitigation must be built-in

The EU AI Act enforces strict data governance and bias mitigation requirements for high-risk AI systems. 

These rules ensure that AI models do not discriminate, are trained on high-quality data, and provide fair and reliable outputs.

The data sets should also have the appropriate statistical properties, including as regards the persons or groups of persons in relation to whom the high-risk AI system is intended to be used, with specific attention to the mitigation of possible biases in the data sets, that are likely to affect the health and safety of persons, have a negative impact on fundamental rights or lead to discrimination prohibited under Union law, especially where data outputs influence inputs for future operations.

EU AI Act, Recital 67

According to Article 10 of the Act, all high-risk AI systems must be developed using training, validation, and testing datasets that meet strict quality standards​.

The idea is to maintain transparency in data collection, processing, and governance to prevent bias and discrimination.

This means your AI models have to:

• Be trained on diverse and representative datasets to avoid bias
• Use reliable, well-documented data sources
• Be continuously monitored for errors and biases

The Act explicitly requires AI developers to proactively detect and correct biases before they deploy high-risk AI systems.

Also, all high-risk AI models must be auditable. Regulators can demand documentation on how AI systems process data at any time.

In short, the EU AI Act ensures AI systems on the EU market are built on fair, unbiased, and explainable foundations.

And that’s exactly how you should approach AI development, anyway.

Human oversight is mandatory for high-risk AI

The EU AI Act makes human oversight a legal requirement for all high-risk AI systems.

The goal is to ensure humans remain in control and can intervene when necessary​.

The regulation states:

High-risk AI systems shall be designed and developed in such a way, including with appropriate human-machine interface tools, that they can be effectively overseen by natural persons during the period in which they are in use.

EU AI Act, Article 14

This means AI can’t be fully autonomous in areas like hiring, healthcare, law enforcement, and finance.

But, why is this so important?

When used right, AI is highly efficient and a game-changer. 

But, if left unchecked, it can also make serious mistakes. Here’s a few common issues:

• Automated decisions can be biased, especially when training data is biased
• AI systems don’t always account for real-life nuances
• Over-reliance on AI, i.e. automation bias, can lead to blind trust in flawed outputs

The EU AI Act prevents these issues by ensuring humans can always intervene and override AI decisions.

Essentially, it mandates a human-in-the-loop system for high-risk AI applications.

source: Miquido

In practice, this means:

• Users must be able to override AI decisions
• Operators must be trained to detect and correct AI errors 
• All high-risk AI systems must have an emergency stop function in case of failure

In short, for high-risk AI, people must always have the final say – no exceptions.

How to ensure compliance with the EU AI Act

Next, we’ll give you some tips on how to meet these requirements.

Build compliance into your AI development lifecycle

Compliance can’t be an afterthought anymore.

The EU AI Act mandates that compliance and risk management must be embedded into every stage of AI development.

According to Article 9, all high-risk AI systems must have a continuous risk management system that runs throughout the AI’s lifecycle.

You’ll have to integrate risk assessments and fairness testing from the first line of code all the way to post-deployment monitoring.

And you’ll need to document everything, too.

Compliance isn’t one-and-done. It’s an iterative process, which requires:

• Risk identification and mitigation at every stage
• Regular updates and audits throughout your AI system’s lifecycle
• Post-deployment monitoring to detect new risks

So, the key here is continuous risk evaluation and monitoring.

You’ll have to identify and mitigate common risks before deployment and implement safeguards against known risks.

A good tip to avoid trouble later is to do risk assessments before you even train your AI model(s).

That’s the best way to ensure your AI model functions safely and fairly over time.

Implement AI governance frameworks

The EU AI Act means that AI governance is a must-have, not a nice-to-have.

You’ll need clear frameworks for compliance, risk management, and ethical AI deployment to stay ahead.

A strong governance framework will help you keep AI development on track and make sure it meets regulatory and ethical standards without slowing innovation down.

But, what exactly does an AI governance framework look like?

A strong AI governance framework includes:

• AI ethics and compliance policies – Defining acceptable AI use and risk thresholds.
• Accountability – Assigning responsibility for AI decisions and failures
• Risk management – Regularly assessing AI impact and mitigating risks.
• Transparency – Ensuring AI decisions can be explained and audited.
• Bias detection – Implement ongoing monitoring of AI models for biases in outputs.

And keep in mind that AI governance is more than just a buzzword – it’s an organization-wide commitment.

Here’s what that looks like:

source: AIGA

The goal of AI governance is that you stay in control of your AI systems, not just react when something goes wrong.

And with the right framework, you’ll reduce risk and build AI that both users and regulators can trust.

Adopt AI transparency best practices

The EU AI Act mandates strict transparency requirements for AI systems, especially high-risk ones.

AI providers have to clearly document how their models work and ensure users understand AI-driven decisions​.

The goal is to ensure that AI decision-making is explainable, traceable, and accountable​.

And the best way to get there is to proactively adopt AI transparency best practices and go beyond just the letter of the law.

Here’s what you should do:

• Allow opt-outs – Always give users the choice to opt-out of AI features and switch to a human.
• Train your team to interpret AI outputs – Make sure your team can accurately interpret AI outputs and recognize errors.
• Regularly test AI models for fairness and bias – Unchecked bias is a lawsuit waiting to happen. Regularly test your AI models and training data for potential bias before deployment.
• Be transparent about AI limitations – Overpromising and underdelivering will back fire. Set clear expectations for what your AI can and cannot do from the start.
• Hire external auditors – A second set of eyes is always good. External auditors can spot risks you might’ve missed.

In a nutshell, the EU AI act is all about building AI systems that users can trust.

And doubling down on transparency is the way to go.

Use third-party compliance tools

Many businesses lack the resources to fully manage complex compliance processes in-house.

And that’s where third-party AI compliance tools come in.

These tools will help you meet the EU AI Act’s requirements without breaking the bank.

So, which tools should you use?

You should start with bias detection tools to analyze your AI models and their training data, like:

• AI Fairness 360 – Open-source toolkit that analyzes datasets for fairness and potential bias
• Google’s What-If Tool – Tests AI decision-making and performance under different scenarios.
• Fairlearn – Helps data scientists and AI developers improve the fairness of AI systems.

This way, you’ll prevent bias from affecting your AI model from the start.

Next, you should integrate AI explainability tools and models, like:

• SHAP (SHapley Additive Explanations) – Provides detailed explanations for AI decisions.
• LIME (Local Interpretable Model-agnostic Explanations) – Helps users understand how AI models make predictions.
• AIX360 – Open-source library with diverse algorithms to help you understand and interpret AI model’s output.

Finally, you should invest in continuous monitoring and MLOps tools like:

• Fiddler AI – Provides real-time insights into your models, helping you catch and fix issues before they escalate.
• Arize AI – Offers tools to monitor and troubleshoot your models, ensuring they perform as expected in the real world.
• Evidently AI – An open-source tool that helps you analyze and monitor your models during development and after deployment.
• Neptune AI – A lightweight MLOps tool that will help you track, manage, and compare AI model experiments in one place.

Using these tools will help you improve accountability and ensure you’re always on top of your AI model’s outputs.

And that’s the best way to avoid regulatory risk.

EU AI Act: FAQs

Need help building compliant AI software?

The EU AI Act is here, and it’s changing how AI is built.

Getting it right is an absolute must. And that’s why you need a reliable AI development partner.

Luckily, you’re in the right place.

We know what it takes to build regulation-ready AI software that’s compliant, reliable, and ready for the real world.

If you want to learn more, feel free to reach out and our team will be happy to set up a meet to discuss your needs in more detail.