2.1 The absence of a dedicated, binding AI law

The most fundamental legal limitation is that, as of mid-2026, Turkey still has no specific statute directly regulating artificial intelligence. AI governance has instead rested on a patchwork of soft law and general legislation—ethical guidelines issued by the KVKK, guidance from the Council of Higher Education (YÖK), sector rules, and the existing Personal Data Protection Law (Law No. 6698). A draft Artificial Intelligence Law was submitted to the Grand National Assembly in June 2024, and further legislative packages have followed, but a comprehensive binding framework has not yet been enacted. This creates legal uncertainty for developers, providers and users, who must infer their obligations from instruments never designed for AI.

The practical consequence is that AI-specific harms are addressed reactively through pre-existing law rather than through purpose-built rules. The 2025 Grok episode illustrates this vividly: when xAI’s chatbot generated content deemed insulting to President Erdoğan, Atatürk and religious values, authorities did not invoke an AI statute but instead applied existing criminal provisions and internet-content enforcement mechanisms, with the telecommunications regulator (BTK) implementing a court-ordered block. The incident confirmed that AI outputs can be treated as actionable content under existing law even without a dedicated regime—an approach that is legally workable but leaves the boundaries of permissible AI speech undefined.

2.2 Gaps in the draft Artificial Intelligence Law

Even where reform is under way, the proposed framework is widely regarded as thin. Commentators reviewing the draft AI law have identified several material gaps:

Even where reform is under way, the proposed framework is widely regarded as thin. Commentators reviewing the draft AI law have identified several material gaps:

2.3 Fragmented oversight and institutional overlap

Turkey lacks a single, dedicated AI regulator. Instead, authority is dispersed across existing bodies: the KVKK for data-related obligations, the BTK for electronic communications and content, YÖK for higher education, and the Ministry of National Education for schools, among others. While each has issued relevant guidance, the absence of a coordinating authority produces a fragmented oversight landscape in which mandates overlap, gaps go unaddressed, and no institution holds end-to-end responsibility for AI assurance. Fragmentation also raises the risk of inconsistent interpretation across sectors.

2.4 Partial alignment with the EU AI Act

Turkey’s draft law explicitly mirrors the structure of the EU AI Act, invoking shared principles of safety, transparency, fairness, accountability and privacy, and borrowing the risk-based classification concept. This alignment is strategically sensible given Turkey’s deep economic ties to the EU and the extraterritorial reach of the Act over Turkish companies serving European markets. However, alignment remains aspirational rather than substantive. Because the Turkish proposal omits the detailed risk definitions, prohibited practices and conformity-assessment machinery that give the EU Act its force, harmonization depends on secondary regulation that has not been drafted. Turkish firms that must comply with the EU Act therefore face a compliance asymmetry: binding, detailed obligations abroad and vague, still-forming obligations at home.

2.5 Data protection: KVKK guidance and its limits

Data-protection law is the most developed strand of Turkey’s AI governance, but it too has limits. The KVKK has issued recommendations on the protection of personal data in AI and, in April 2026, published guidance on "agentic" AI that treats derived and inferred data—outputs produced by correlating multiple datasets—as personal data subject to full compliance under Law No. 6698. The KVKK has also stressed that decisions based solely on automated processing that significantly affect an individual must permit human review or objection.

These are meaningful safeguards, yet they operate through guidance and the general data-protection statute rather than through AI-specific legislation with proportionate enforcement tools. Guidance can clarify expectations but does not carry the same binding weight or tailored sanctions as primary law, and treating broad categories of inferred data as personal data raises practical compliance burdens that are difficult to meet without clearer rules. The result is a governance model that is comparatively robust on privacy but narrow: it addresses the data dimension of AI while leaving safety, transparency and accountability dimensions comparatively under-regulated.

3.1 Algorithmic bias and the low-resource status of Turkish

A structural limitation with deep ethical implications is that Turkish is, in machine-learning terms, a low-resource language. It is highly agglutinative and morphologically complex, and it lacks the large, high-quality annotated datasets available for languages such as English. Models built for analytic languages do not transfer straightforwardly, and many Turkish training and reasoning datasets are machine-translated from English without validation, propagating inaccuracy and bias.

This has concrete fairness consequences. Research investigating gender bias in Turkish language models has documented biased associations, and studies of natural-language-processing systems have found that dialect-specific Turkish structures are frequently misclassified or misparsed by tools such as spaCy, Stanza and BERTurk. Because bias-mitigation techniques that work for high-resource languages are less effective here, Turkish requires alternative approaches such as contextual word alignment. Until such methods mature, AI systems deployed in Turkish risk encoding linguistic and social bias, disadvantaging dialect speakers and reinforcing inequities—an ethical limitation that also deepens the digital divide discussed below.

3.2 State surveillance and the erosion of civil liberties

Perhaps the most acute societal concern is the rapid, largely ungoverned expansion of AI-enabled state surveillance. Reporting indicates that Turkey substantially enlarged its surveillance capacity in 2024−2025, procuring some 3,500 facial-recognition cameras for deployment across 30 provinces and issuing tenders for roughly 13,000 additional devices, alongside plans for body-worn cameras with facial-recognition capability. AI has also been integrated into policing and justice functions ranging from automated traffic enforcement to predictive analytics.

Academic and human-rights analyses warn that indiscriminate facial recognition—particularly the scanning of crowds or protests—creates unacceptable risks to privacy, imports racial and other biases, and lacks adequate social control. There are reports that facial recognition has been used to identify protesters. Deployed at scale and without the legal safeguards that a dedicated framework would provide, these systems threaten civil liberties, the presumption of innocence and freedom of assembly. The limitation here is not technical capacity but the absence of proportionality, transparency and oversight, which turns a powerful tool into a hazard for democratic rights.

3.3 Free expression and the precedent of the Grok ban

The 2025 Grok ban is significant not only as a regulatory case study but as an ethical and free-expression flashpoint. When a Turkish court ordered access to Grok’s content to be blocked and the BTK implemented the order, Turkey became the first country to censor an AI chatbot on such grounds. Anti-censorship advocates have cautioned that the precedent could be invoked by other governments to justify blocking AI outputs they find objectionable under broad definitions of hate speech or national security. The episode highlights a genuine tension—AI systems can generate defamatory or unlawful content—while also illustrating how, absent clear and narrowly drawn rules, content controls can chill legitimate expression and be applied to protect officials from criticism.

3.4 The digital divide and unequal access

AI’s benefits in Turkey are unevenly distributed. The low-resource status of Turkish, combined with disparities in connectivity, digital literacy and access to advanced tools, means that the advantages of AI accrue disproportionately to well-resourced institutions and urban, educated populations. Researchers working on open-source machine translation for Turkish and related Turkic languages frame their efforts explicitly as attempts to reduce digital inequality—an implicit acknowledgment that current systems underperform for many speakers. Without deliberate intervention, AI risks widening rather than narrowing existing social and regional gaps, a societal limitation with long-term equity implications.

3.5 Public trust, transparency and academic freedom

Finally, the effective adoption of AI depends on public trust, which remains fragile in an environment of limited transparency. The draft law’s emphasis on transparency, fairness and accountability is intended precisely to build such trust, but principles unaccompanied by enforceable mechanisms may not suffice. In the academic sphere, YÖK has issued an Ethics Guide for Generative AI in Scientific Research that prohibits listing AI as an author and requires disclosure of AI assistance, while the Ministry of National Education’s 2025−2029 Action Plan mandates ethical review and model-transparency requirements for AI tools in public schools.

These measures are constructive, yet they also surface tensions. Requirements for disclosure and vendor evaluation must be balanced against researchers' autonomy and the risk that opaque or centralized certification processes could constrain academic freedom. More broadly, in a context where state surveillance is expanding and content controls have been exercised against AI outputs, scholars and citizens may reasonably worry about self-censorship. Trust, transparency and academic freedom are therefore interdependent: weakness in one propagates to the others, and each remains a live limitation on Turkey’s AI trajectory.