The Criminal Liability Trap in AI Regulation: Turkey’s Draft vs. the EU Model
I. Introduction
AI regulation becomes most difficult when it touches criminal liability and freedom of expression. In generative systems (LLMs), users interact through prompts, outputs are not fully predictable, and the same input can produce different results depending on context and model settings. If the law treats every output as if it was directly written by the user, liability can drift from unlawful speech to the user’s intent in an uncertain, probabilistic process. In practice, that risk often leads to self-censorship and over-filtering.
In our earlier article, Regulating AI Speech in Turkey: Lessons From the Grok Ban and 2025 Draft Laws, we examined the Grok episode and Turkey’s late-2025 draft package. Building on that baseline, this article uses the Turkey vs EU comparison to stress-test the hard questions: how intent can be proven when prompts do not guarantee outcomes, how developer exposure fits with the principle of personal criminal liability, and how enforcement choices can narrow lawful use even without explicit bans.
II. Two Different Approaches to the AI Problem
2.1. The EU: governance duties, oversight, and fines that scale
The main focus in the EU AI Act is governance. The emphasis is on clear duties, transparency, risk management, documentation, and on supervisory enforcement backed by fines that are meaningful for large, cross-border operators.
In practice, this means the EU is less focused on any single incident and more focused on whether the operator can show control. When problems emerge, the question is usually: what safeguards were built in, what was tested, what was monitored after deployment, and what changed once the risk was visible? The expectation is not perfection. It is disciplined, provable mitigation that matches the scale and sensitivity of the deployment.
This is also why EU-style enforcement can rely on fines as a real lever. The internal market is large, supervision is structurally coordinated, and penalties can be calibrated to turnover. For companies, the compliance pressure often lands on process: being able to demonstrate, in a credible way, that risk was assessed and managed rather than ignored.
2.2. Turkey: access restriction remedies and criminal liability
Turkey operates in a different enforcement reality. Major AI developers, model operators, and core infrastructure providers are frequently located abroad. In that setting, monetary sanctions may exist on paper, but they are not always the most effective lever in situations framed as urgent.
That is why Turkey’s toolkit tends to place practical weight on remedies that can be executed locally and quickly: content removal, geo-blocking, and access restrictions. The Grok episode discussed in our prior article reflected that logic clearly: the immediate pressure point was not collecting a fine abroad, but stopping dissemination in Turkey.
What makes the late-2025 draft package more legally sensitive is that it does not stop at intervention mechanics. It also attempts to connect certain AI-related harms to criminal attribution in ways that can create real exposure for user-side prompting and, in certain scenarios, developer-side design and training choices. Even before implementation details settle, this framing alone materially changes the compliance risk profile for global operators.
2.3. Why this difference matters
These approaches create different incentives. The EU model tends to drive governance-heavy compliance: more testing, more documentation, more monitoring, clearer internal controls, and an evidence trail that can stand up to supervisory scrutiny. Turkey’s direction tends to drive response-heavy compliance, but with a sharper edge: when criminal exposure is in play and access restrictions are a realistic lever, companies have strong incentives to be conservative—tighten filters, restrict sensitive categories, reduce sharing functionality, or deploy jurisdiction-specific settings to avoid escalation.
The potential problems in these regulations are easy to predict. If standards are too open-ended, companies will respond defensively—by over-removing, over-filtering, or limiting features. If standards are too narrow, meaningful harms fall through the cracks. The legal challenge is finding the optimal ground: rules that are enforceable in practice, but still disciplined enough to avoid outcome-driven liability and routine over-restriction.
III. Turkey’s Draft: Criminal Liability and the Real Problem
3.1. What the draft is trying to do (in plain terms)
Turkey’s late-2025 draft package does more than expand removal and access-blocking tools. It also tries to connect AI use to criminal liability.
The draft follows a simple structure:
- User-side liability: if a person uses an AI system to produce something that is already a crime under Turkish law, the person can be treated as the offender. The AI is framed as the tool.
- Developer-side exposure: the draft also points toward higher exposure for developers where the system’s design or training is seen as enabling certain offences.
This approach is meant to close the “accountability gap” created by non-human output. But once criminal liability is tied to prompts and model design, the legal questions become much harder than in ordinary platform content cases.
3.2. Free speech risk: why “prompts” make the boundary harder to draw
In an AI setting, the user interacts with the system through a prompt, the input the user types to get a response. The model then produces an output. That output may stay private, or it may become public if it is posted, shared, or shown through a platform feature. This structure matters legally because it raises a simple question: is the law reacting to something that was actually expressed publicly, or to a user’s attempt to test and steer a system before anything was published?
This is also why the phrase “unlawful speech” needs to be handled with care. All legal systems restrict some kinds of expression, especially where it becomes a real harm, such as direct threats, targeted harassment, or incitement. But the line is not fixed. In practice, what counts as “unlawful” depends on the offences in that jurisdiction and how broadly concepts like “public order” are applied. If the definition is drawn too widely, the impact is not limited to a few removals or prosecutions. Which can result in users and companies avoiding lawful speech that could be interpreted as risky, or in other words, auto censorship.
This is where the draft’s user-side criminal framing becomes sensitive. If liability sits too close to the prompt, the law can start to punish inquiry rather than expression. Prompts are often used for testing, satire, translation or hypotheticals. When that upstream behavior becomes the main trigger, overreach becomes more likely. At the same time, platforms may respond defensively and introduce tighter filters, narrower topic coverage, and “Turkey settings” that restrict lawful uses to avoid escalation.
3.3. Intent and proof: prompting is not the same as writing the message
A prompt can influence the output, but it does not give the user full control. If the user cannot reliably foresee what the model will produce, treating the output as the user’s own statement becomes problematic for criminal attribution.
To illustrate this with an example: the author of a threatening email actually controls the words and fully decides what is written in the email. Whereas with an AI system, the output is produced by a probabilistic model shaped by training data, system instructions, safety filters, and context. Even a carefully written prompt does not guarantee a specific output. The same prompt can produce different results depending on the model version, settings, language, or small phrasing changes.
That makes intent harder to prove. A single unlawful-looking output does not automatically show that the user intended that exact result. The same output can appear because of deliberate steering, but it can also appear because the prompt was ambiguous, the context shifted, the translation changed the meaning, or the model behaved in an unexpected way.
If criminal sanctions are involved, this is not a technical detail. Criminal law works on proof beyond reasonable doubt. In AI cases, that usually requires looking at the full picture, such as what the prompt actually asked for, whether the user repeatedly tried to steer the model toward unlawful content, whether the result can be reproduced in the same system context, and what the user did next.
The draft signals an intent to treat “direction” as a basis for criminal exposure. But it does not yet explain how prompt-output cases should be assessed in evidence terms. Without clear standards, there is a real risk that enforcement becomes outcome-driven: the output looks unlawful, therefore the user must have intended it.
3.4. Developer exposure: the “personal criminal liability” line
Developer-side exposure is even more sensitive. Developers do not author each output the way a human authors a statement. They build and deploy a system that behaves differently depending on prompts, context, language, and safety configuration.
Here, a basic principle becomes important. Criminal liability is generally personal, and in many legal systems it is tied to constitutional or fundamental rights safeguards. In practical terms, criminal punishment should be based on the person’s own culpable act and fault. A model that imposes criminal exposure on a developer merely because an unlawful output occurred, without a clear showing of fault, risks violating the constitutional principle.
For developer exposure to be sustainable, it needs clear limits. In an AI setting, it should require more than showing that a harmful output existed. It should point to fault, such as knowing enablement of unlawful use, intentional facilitation, or reckless disregard of repeated and documented failure modes.
Otherwise, the developer becomes a guarantor of what a probabilistic system might say. That is difficult to justify in criminal-law terms.
IV. Conclusion
AI systems can generate harmful content quickly, at scale, and across borders. Both the EU and Turkey are responding to this reality, but they are doing so with different drastically different approaches. The EU’s model is built around governance. It pushes operators toward transparency, risk management, documentation, and supervision, with fines that can actually move behavior in a large internal market. Turkey’s draft package, by contrast, places more practical weight on fast intervention at the access layer, and it also tries to connect certain AI scenarios to criminal attribution for users and, in some cases, developers.
The criminal law aspect is where the problems arise. A prompt can influence an output, but it does not give the user full control or full predictability. The same is true for developers. Developers build and deploy probabilistic systems, but they do not author each statement the system later generates in response to changing prompts, contexts, and settings. If criminal exposure attaches too closely to the output, without a clear fault-based standard and an evidence approach that can reliably prove intent, enforcement risks becoming outcome-driven.
In practice, this kind of uncertainty tends to push the market in one direction. Operators do not wait for case law to clarify where the line is. They reduce risk upfront by tightening filters, narrowing sensitive categories, and limiting features in the local market, especially where access restrictions are a realistic lever. While this may reduce certain harms, it can also narrow lawful use and legitimate expression, not because the law demands it explicitly, but because the safest product decision is often the most restrictive one. The long-term test for the draft will therefore be whether it can deter deliberate misuse without turning ordinary prompting and routine product design into a source of criminal exposure.
