The 'Accountability Gap': Why AI Regulation Isn't Enough Without Strong Enforcement

The passage of the EU AI Act was a landmark achievement—a historic, continental-scale attempt to build guardrails for our most transformative technology. For the first time, we have a comprehensive legal framework that moves AI governance from abstract principles to concrete, risk-based rules.

But let’s be clear: although passing the law demands a strong political effort, it is the easy part.

The real test, the one that will determine whether the Act becomes a global standard for safety or a case in "paper compliance," begins now. And as I see technology evolve, I perceive a dangerous gap forming between the law as written and our capacity to enforce it. This is the Accountability Gap, and closing it is the most urgent task facing policymakers in AI Regulation today.

The Lure of "Paper Compliance"

From my time leading growth and data strategy at major companies, I can tell you that corporations respond to incentives and risks, most often targeting investors behaviors. Faced with complex new regulations, the default first step is likely to be the minimal optimization of compliance possible, aiming the lowest possible cost and effort, taking advantage of any abiguity or legal breach. The following path, then, depends on enforcement: if the first effort is proven to be enough, they tend to keep it at the same level. If they perceive a risk in not commiting further, they will perfect their compliance to the regulation requirements.

The current European regulatory landscape practically invites this behavior. The inherent friction between the AI Act’s mandate to audit for bias and the GDPR’s strict rules on processing sensitive data creates a "regulatory ambiguity". A manager faces a dilemma: to risk non-compliance with the AI Act by failing to test for bias adequately, or to risk a GDPR violation by processing the very data needed to do so.

In an environment of weak or technically unsophisticated enforcement, the choice of many firms may be "paper compliance." They will generate the required documentation, produce plausible-sounding reports, and check the necessary boxes. But they may avoid the deep, costly, and difficult work of truly re-engineering their systems for fairness and safety. They will create the illusion of compliance.

You Can't Regulate What You Don't Understand

This accountability gap is widened by a profound technical reality. There are often fundamental mismatches between what technical methods for machine unlearning can achieve and aspirations for... law and policy aims. To take one single law requirement as an example, we can study the legal right to be forgotten, the right to require your data is erased. Perfect removal of data or bias is often technically infeasible, and policymakers may need to specify their expectations to go beyond "reasonable best efforts" and enforce effective guarantees.

In the case of data deletion, we need to consider that data is not simply stored at a table in a CRM anymore. It is usually used to train algorithms or automatize tasks. From a technical perspective, unlearning or forgetting is commonly distinguished into two distinct and often confused goals:

1. Observed Information Removal: Attempting to make the model behave as if it had never seen a specific piece of data during its training. This is the ideal of "forgetting," but it is extremely difficult to achieve and verify perfectly.

2. Output Suppression: Preventing the model from generating specific information in its responses. This is a more pragmatic and technically feasible goal. For those who used generative AIs in the early days: remember how they wouldn't respond to controversial topics, like politics, given that sources about politics were biased? This is a type of Output Suppression.

What happens when a regulator who doesn't understand this technical nuance is tasked with auditing a "high-risk" AI system?

They will be shown a report, not the model's internal state. They won't see that the system's learned feature representations are systematically biased by extraneous properties unrelated to its task. They won't be able to distinguish between a system that is genuinely fair and one that has simply been fine-tuned to pass a superficial test. Without the ability to "speak data," the enforcement body becomes a passive recipient of paperwork, not an active auditor of technology.

Closing the Gap: A Call for Capable Enforcement

The EU AI Act is a foundation, but it is not the building. To construct a truly safe and trustworthy AI ecosystem, we must build enforcement bodies with the power, resources, and expertise to close the accountability gap. This requires three non-negotiable elements:

1. Sufficient Funding: Meaningful oversight is expensive. It requires top-tier technical talent and significant computational resources. Governments that are willing to invest billions in deploying AI to improve public services must be willing to invest a meaningful fraction of that to oversee it effectively.

   
   

2. Deep Technical Proficiency: Enforcement bodies cannot be staffed solely by lawyers and policy experts. They must hire data scientists, ML engineers, and AI ethicists who can perform deep technical audits, interrogate models, and understand the statistical evidence of bias. As I argued in a recent article on biometric regulation, these must be prior and independent algorithmic impact audits.

   
   

3. Structural Independence: To be seen as credible by both the public and the industry, these auditing bodies must be shielded from political and corporate pressure. Their independence is the ultimate source of their authority and the bedrock of public trust.

   
   

The work of AI governance is just beginning. We have written the rules of the road; now we must build a patrol that is capable of enforcing them. Without strong, technically proficient, and independent enforcement, the AI Act risks becoming a noble failure—a powerful symbol with no real power. The human impact of that failure is a cost we cannot afford to pay.

The journey from law to practice is navigated by incentives. Remember the corporate focus on investor behavior? Strong and visible law enforcement is the critical ingredient that shapes this landscape. As the first enforcement actions under the AI Act begin to make headlines, the abstract risk of non-compliance will transform into a concrete factor that investors must price into their valuations. Only then, when the cost of inaction is clearly reflected in market dynamics, will companies be truly motivated to move beyond paper-thin reassurances and embrace the deep, structural changes required for genuine AI safety and fairness.

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