Is the AI Alignment Problem Adequately Addressed by European Legislation?

AI alignment isn't just about science fiction scenarios, but about the daily question: does this system truly do what we intend, within boundaries that fit human values, fundamental rights and safety?

What the EU AI Act Does Address on Alignment

The EU AI Act isn't an "alignment law" in the technical sense, but it does address components that organizations often experience as alignment problems: drift, unintended output, bias, manipulation, opacity and insufficient human intervention.

1) Prohibited Practices as Hard Boundaries

The AI Act draws a line at AI applications considered unacceptable, precisely because they can steer behavior or affect rights. This first set of obligations already came into effect early in the phased implementation. (EUR-Lex AI Act)

2) High-risk Requirements as "Alignment-by-Design"

For high-risk systems (such as in employment, education, critical infrastructure, healthcare, credit and public contexts) the AI Act builds a package of requirements that can be read as a set of organizational alignment controls:

3) General-purpose AI and 'Systemic Risk'

The AI Act recognizes that generic models (GPAI) end up downstream in countless applications, making alignment not just an "application question" but also a "model question". Therefore, specific obligations exist and a GPAI Code of Practice has been published that makes compliance concrete, with separate attention to safety and security for models with systemic risk. (EC Digital Strategy)

This is an important point: Europe tries to place alignment not just with the end user or the deployer, but also to organize it upstream through documentation, transparency and safety practices for the more powerful model category.

Where Legislation Falls Short

Even with the AI Act, there remains a gap between "compliance" and "alignment" in the fundamental sense.

1) Legislation Cannot Fully Regulate Goal Misalignment

A law can require you to manage risks, organize oversight, document and monitor. But if a model learns unexpected strategies, or if capability jumps lead to new behavior, that's not fully coverable with process obligations. The AI Act pushes organizations toward mature governance, but doesn't guarantee intrinsic "value alignment" of models.

2) Timing and Enforceability Remain Variable

In theory, the phased implementation is precisely meant to give parties time. In practice, it also creates a period where the strongest obligations haven't yet "landed" everywhere.

3) Liability: A Missing Link

Alignment isn't just about prevention, but also about incentives after the fact: who pays the damage when things go wrong? Precisely there, the European route is mixed.

In short: Europe does have a solid product and safety track, but a specific civil AI liability track has dropped off, making the total incentive structure less complete.

Alignment is Also "Encircled" by Other Laws

The AI Act doesn't stand alone. Part of alignment-like risks is addressed elsewhere:

This whole makes the European response broader than just "the AI Act", but also more fragmented: alignment components are spread across multiple regimes.

Three Practical Situations: What's Covered and What Isn't

1) HR and Recruitment with a Generic Model Under the Hood

Suppose: an organization uses a tool that summarizes application letters, ranks candidates and suggests interview questions. Alignment questions then are: does the system rank on relevant criteria, is there bias, do recruiters understand the limits, and can they deviate with justification?

The AI Act pushes toward risk management and human oversight (certainly if it falls under high-risk), while upstream GPAI documentation and transparency help to better understand downstream risks. But: if the tool stays "just under" high-risk or is purchased as a feature through a gray area, much remains dependent on internal governance.

2) Healthcare Triage and Prioritization

With triage, it's not just about accuracy, but also about failsafes, escalation, audit trails and accountability. The AI Act helps primarily by requiring structure: document, monitor, take incidents seriously, and make human decision-makers truly authorized.

3) Recommendation Algorithms on Platforms

Here "alignment" touches societal effects: polarization, disinformation, manipulation and harmful engagement loops. The DSA places obligations around risk assessment and mitigation of systemic risks for very large players, including fundamental rights risks. (Digital Services Act)

The AI Act isn't always the primary instrument here. As a result, you get: strong obligations for a subset of platforms, but less clear grip on comparable effects at smaller players or new distribution forms.

What This Comes Down To

At the same time, "alignment" in the deeper safety sense remains only limitedly legally enforceable. The EU is taking steps through GPAI obligations and the Code of Practice, but part remains dependent on technical state-of-the-art, supervisory capacity and the political choice of how strictly and quickly the rules are actually applied.

And because a specific AI liability directive was withdrawn, the incentive mechanism after the fact is less specifically developed than originally intended.

Sources

🎯 More on Responsible AI: Check out the Responsible AI Implementation Guide for practical frameworks and best practices.