TOBB Gençol – Turkish AI Law & Governance Workshop / Maltepe University

The first Global Dialogue on AI Governance will be a success if it moves beyond declaratory consensus and produces three tangible deliverables. First, a Regulatory Interoperability Mapping that documents, in a structured and comparable format, where national and regional AI governance frameworks converge and where they diverge. Current fragmentation is not merely a policy inconvenience; it generates measurable compliance costs that disproportionately burden small and medium-sized economies. A shared diagnostic of this "regulatory interoperability debt" is the prerequisite for any meaningful harmonization effort. Second, a Capacity-Building Commitment Mechanism specifically targeting middle-income countries that possess industrial capacity and institutional infrastructure but lack the specialized regulatory workforce to implement AI governance at sector level. These countries including Türkiye, Brazil, Indonesia, Mexico, South Africa represent the governance gap most likely to determine whether international AI norms achieve global traction or remain confined to a handful of jurisdictions. The Dialogue should produce a concrete pledge framework, analogous to climate finance commitments, for technical assistance in AI regulatory capacity. Third, a Sectoral Reference Architecture acknowledging that AI governance cannot be effectively pursued through horizontal regulation alone. Critical infrastructure sectors energy systems, healthcare, financial services, logistics present fundamentally different risk profiles, stakeholder ecosystems, and failure consequences. The Dialogue should mandate at minimum a working methodology for translating general AI principles into sector-specific governance frameworks, drawing on existing national experiences. Finally, success requires that the Dialogue establishes a structured feedback mechanism between the Geneva and New York sessions, enabling iterative refinement rather than treating each convening as a standalone event. Without institutional continuity, even the best outcomes will dissipate before implementation.

• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

These four priorities emerge directly from our experience developing a 519-page, seven-sector national AI policy framework for Türkiye, covering financial technology, healthcare, education, media, energy, logistics, and AI literacy. Interoperability is our foremost concern because Türkiye occupies a structural position shared by many emerging economies: bound to the EU regulatory ecosystem through the Customs Union and Council of Europe membership, yet needing policy space for innovation-first approaches suited to a developing AI ecosystem. Our energy sector framework, for instance, deliberately diverges from the EU AI Act's risk-based classification by introducing negative-weight parameters for innovation potential and domestic development capacity. This is not regulatory arbitrage; it is calibrated adaptation. The Dialogue must create space for such principled divergence while maintaining interoperability. Capacity-building is selected because our cross-sectoral analysis revealed that the binding constraint is not technology but regulatory human capital. Türkiye's energy sector needs an estimated 5,600-7,300 specialized AI professionals by 2030, against a current base of approximately 1,100. Similar gaps exist in every sector we examined. Transparency and human oversight reflects our conviction, grounded in sector-specific analysis, that meaningful oversight requires differentiated models rather than a single standard. Our energy framework defines four distinct human oversight models. Human-in-the-Loop, Human-on-the-Loop, Human-above-the-Loop, and a prohibited category of Human-out-of-the-Loop for critical infrastructure each calibrated to specific operational contexts. Social and linguistic implications is critical because AI governance discourse is conducted almost exclusively in English, while the populations most affected by AI deployment operate in languages that remain computationally under-resourced. Turkish, spoken by over 85 million people, still lacks adequate NLP benchmarks for high-stakes applications such as automated essay scoring and judicial decision support.

Three cross-cutting issues require explicit attention beyond the listed themes. First, sector-specific AI governance as a distinct discipline. The current discourse overwhelmingly treats AI governance as a horizontal regulatory challenge. Our seven-sector analysis demonstrates that this assumption is operationally inadequate. An AI system managing national grid frequency control at 50 Hz ± 0.05 Hz tolerance presents fundamentally different governance requirements than an AI content recommendation algorithm, even if both are classified as "high-risk" under the same horizontal framework. The energy sector alone required 34 dedicated chapters and 50 regulatory articles in our framework. The Dialogue should explicitly acknowledge the necessity of sector-specific governance architectures and facilitate exchange of national experiences in building them. Second, the "regulatory interoperability debt" accumulating between jurisdictions. As the EU AI Act, China's tiered regulations, the UK's principles-based approach, and emerging frameworks in Türkiye, Brazil, India, and the Gulf states develop independently, implicit incompatibilities are compounding. These are not merely theoretical concerns they generate concrete compliance costs for companies operating across jurisdictions and create governance vacuums in cross-border AI deployments such as energy grid interconnections and logistics corridors. The Dialogue should commission a systematic mapping of these divergences before they become structurally entrenched. Third, critical infrastructure AI as a national security dimension. AI systems controlling energy grids, water treatment facilities, nuclear safety monitoring, and transportation networks occupy a governance category that transcends conventional risk classification. Our framework introduces an "ultra-high-risk" tier specifically for AI systems whose failure could affect over one million citizens, trigger cascading infrastructure failures, or compromise nuclear safety boundaries. This category absent from current international frameworks deserves dedicated multilateral attention, particularly regarding cross-border infrastructure such as interconnected electricity grids and transnational pipeline networks.

Türkiye exemplifies the governance challenges facing middle-income industrial democracies navigating AI adoption without a mature domestic regulatory framework. Interoperability gaps impose direct economic costs. Türkiye's Customs Union with the EU creates a binding obligation to harmonize with the Machinery Regulation (2023/1230), which now covers AI-controlled equipment. Yet the EU AI Act itself is not directly binding, producing a regulatory asymmetry: Turkish manufacturers must meet EU technical standards for export while domestic AI deployment lacks equivalent governance. Our energy sector analysis quantified this tension — the EU's broad "high-risk" classification for critical infrastructure AI would capture virtually all energy applications, including low-risk innovation tools, potentially deterring the sector where only 8 percent of enterprises have integrated advanced AI systems. Capacity deficits are sector-specific and measurable. Our national assessment identified that Türkiye's energy sector alone requires 5,600–7,300 specialized AI professionals by 2030, against a current base of approximately 1,100. The logistics sector shows AI adoption at just 5 percent, despite managing trade corridors carrying 80 percent of national commerce by volume. These gaps cannot be addressed through generic digital skills programs; they demand sector-specific governance expertise that barely exists globally. Transparency frameworks lag behind deployment. Türkiye's electricity distribution network — 21 privatized companies serving 85 million citizens — loses an estimated 15–20 billion TL annually to technical and non-technical losses. AI-based detection systems are being deployed, yet no national standard exists for algorithmic transparency, audit trails, or consumer redress mechanisms when AI-driven decisions affect household energy access. Linguistic exclusion compounds every gap. Turkish, serving over 85 million speakers, remains computationally under-resourced for high-stakes AI applications, rendering governance frameworks designed around English-language benchmarks structurally inadequate.

The AI Dialogue can fulfill a role that no existing mechanism currently occupies: serving as the operational bridge between high-level AI principles which are abundant and sector-level implementation which remains fragmented and nationally isolated. First, the Dialogue should function as a regulatory learning exchange, not merely a negotiating forum. Countries are currently developing AI governance frameworks in parallel with minimal structured learning from one another's implementation experiences. Türkiye's development of a seven-sector AI policy framework revealed that the most valuable international insights came not from general principles but from specific sectoral governance failures and adaptations the UK's Ofqual 2020 algorithmic grading crisis informing our education framework, Ukraine's 2015–2016 grid cyberattacks shaping our energy cybersecurity protocols, the EU's CBAM timeline forcing our carbon-AI integration. The Dialogue should systematize this cross-national sectoral learning. Second, the Dialogue should establish a Governance Interoperability Assessment function. Before harmonization can be pursued, the actual landscape of convergences and divergences must be mapped with technical precision. Our Customs Union experience demonstrates that interoperability challenges are granular and sector-specific the EU Machinery Regulation creates binding obligations for AI-controlled energy equipment, while the AI Act's risk classification creates only referential influence. These distinctions matter operationally but are invisible in high-level governance discussions. Third, the Dialogue should create a structured pathway for middle-income countries to contribute governance innovations, not merely adopt frameworks designed elsewhere. Our energy sector classification model which introduces innovation-capacity and domestic-development parameters alongside risk assessment represents a governance approach specifically designed for developing AI ecosystems. Such innovations deserve international evaluation on their merits rather than being treated as deviations from a presumed Northern template. The Geneva–New York sequencing provides a natural structure: Geneva for technical-sectoral exchange, New York for political-institutional commitments.

The AI Dialogue should build upon without duplicating several existing mechanisms, each of which addresses a necessary but insufficient dimension of the governance challenge. The OECD AI Policy Observatory provides the most comprehensive cross-national monitoring but is limited to OECD membership and operates primarily at the horizontal policy level. The AI Dialogue's added value is universal membership and sector-specific depth. The Global Partnership on AI (GPAI) advances technical and applied research but lacks the intergovernmental mandate to address regulatory interoperability between jurisdictions. The AI Dialogue can provide the political legitimacy that GPAI's expert outputs require for implementation. The ITU AI for Good platform facilitates project-level engagement but does not systematically address governance architecture. The co-location of the first AI Dialogue session with AI for Good week in Geneva creates an immediate opportunity for structured connection. The Council of Europe Framework Convention on AI (CETS No. 225) establishes binding human rights obligations for signatory states including Türkiye but its scope is limited to public sector AI and does not extend to sectoral governance of critical infrastructure or market regulation. The AI Dialogue can address this gap. At the regional level, the EU AI Act implementation process is generating governance precedents that will shape global expectations. The AI Dialogue should establish a formal mechanism for non-EU countries particularly those with structural ties such as Customs Union partners to engage with this process rather than passively receiving its extraterritorial effects. The critical added value of the AI Dialogue is its unique combination of universal UN membership, the General Assembly mandate of Resolution 79/325, and the explicit inclusion of all stakeholder categories. No existing mechanism combines these three attributes. This positions the Dialogue as the only forum capable of producing governance frameworks that are simultaneously legitimate, technically grounded, and globally applicable.

The AI Dialogue's effectiveness depends on structuring participation around demonstrated governance experience rather than institutional status alone. Format recommendation: Sector-specific working tracks alongside plenary sessions. The Geneva session should operate through parallel sectoral tracks energy and critical infrastructure, healthcare, financial services, education, media, logistics each convening practitioners who have built or implemented governance frameworks in that domain. Our experience producing sector-specific policy frameworks revealed that the most productive governance insights emerge from cross-national comparison within a single sector, not from cross-sectoral discussion at the abstract level. A healthcare AI regulator from Brazil and one from Türkiye will generate more actionable output in two hours than either would in a full day of general governance discussion. Stakeholder contribution model: Evidence-based submissions with implementation experience. The Dialogue should weight contributions that present concrete governance architectures, pilot results, and implementation challenges over position statements articulating general principles. A structured submission template problem identified, governance mechanism designed, implementation outcome, lessons learned would dramatically increase the actionability of stakeholder inputs. Academic and civil society integration: Dedicated analytical function. Rather than limiting academia to panel participation, the Dialogue should commission independent analytical synthesis of submitted inputs before each session. This transforms academic participation from commentary into infrastructure. Youth and emerging professional inclusion: Substantive roles, not ceremonial ones. Young researchers and professionals leading governance initiatives including student-led policy workshops producing deliverables adopted by government ministries should participate as contributors on equal footing, not in segregated "youth sessions" that operate parallel to rather than within the main Dialogue. Inter-sessional continuity: Digital working groups between Geneva and New York. Thematic working groups should operate continuously between sessions, producing iterative drafts that each physical convening refines rather than restarts.

Three categories of voices are systematically underrepresented in global AI governance discussions. First, middle-income industrial democracies. Current discourse is dominated by two poles: advanced economies designing regulatory frameworks and least-developed countries positioned as capacity-building recipients. Countries like Türkiye, Brazil, Indonesia, Mexico, Thailand, and South Africa which possess significant industrial bases, functioning institutions, and growing AI ecosystems occupy a governance space that is neither adequately represented nor well understood. These nations face the most complex governance challenge: building regulatory frameworks sophisticated enough for an industrializing AI sector while avoiding compliance burdens that would stifle ecosystems still in formation. Our experience designing an innovation-first classification model with negative-weight parameters for domestic development capacity emerged precisely from this underrepresented position. Second, sectoral regulators and operators from non-English-speaking jurisdictions. Energy grid operators, healthcare regulators, financial supervisors, and logistics authorities possess irreplaceable operational knowledge about where AI governance succeeds and fails in practice. Yet their participation in international forums is constrained by language barriers, travel budgets, and institutional mandates that do not include international engagement. The Dialogue should establish funded sectoral delegations enabling regulatory practitioners not only diplomats or policy generalists to participate. Third, communities affected by AI deployment in linguistically under-resourced contexts. Over 85 million Turkish speakers, 270 million Indonesian speakers, and hundreds of millions of speakers of other computationally under-resourced languages experience AI systems designed, trained, and evaluated primarily in English. Governance frameworks that do not account for this linguistic asymmetry are structurally incomplete. Inclusion mechanisms: simultaneous interpretation beyond the six UN languages for sectoral sessions, funded participation for regulatory practitioners from middle-income countries, and mandatory linguistic impact assessment as a component of governance framework evaluation.

Five formats could transform the AI Dialogue from a conventional intergovernmental event into a genuinely productive governance laboratory. Governance Stress-Test Sessions. Rather than presenting finished frameworks, participating countries submit their AI governance architectures to structured peer review. Reviewers from different regional and developmental contexts identify gaps, contradictions, and interoperability challenges. Our seven-sector framework would benefit enormously from systematic scrutiny by practitioners from jurisdictions with different structural conditions and so would theirs from ours. Cross-Jurisdictional Sandbox Simulations. Participants collaboratively work through a realistic governance scenario such as an AI system managing cross-border energy grid interconnections, or an algorithmic trading system operating across multiple financial jurisdictions and attempt to identify which existing national frameworks apply, where they conflict, and where governance vacuums exist. This makes "regulatory interoperability debt" tangible rather than theoretical. Rapid Sectoral Diagnostics. Each sectoral track produces, within the session timeframe, a structured two-page diagnostic identifying the three most critical governance gaps in that sector globally and three concrete actions the AI Dialogue can take before the next session. Tight deliverable deadlines force prioritization over comprehensiveness. Regulatory Practitioner Exchanges. Pair sectoral regulators from different regions for structured bilateral exchanges. An energy regulator from Türkiye's EPDK paired with Brazil's ANEEL, or a health data authority from Indonesia paired with one from South Africa, generates more implementation-relevant insight than any plenary panel. Living Document Platform. Establish a digital workspace where submitted governance frameworks, sectoral diagnostics, and peer review outputs accumulate as a continuously updated resource between sessions transforming the Dialogue from a periodic event into a persistent governance infrastructure.

We offer five concrete governance mechanisms developed through our seven-sector national AI policy framework for Türkiye, each addressing a specific governance challenge with an implementable solution. 1. Innovation-First Classification with Negative-Weight Parameters. Our energy sector framework classifies AI systems across four risk tiers using five parameters. Critically, two parameters innovation potential and domestic development capacity carry negative weights, meaning that high-innovation or domestically developed systems receive proportionally lighter regulatory burdens at equivalent risk levels. This structurally incentivizes local AI development within the regulatory framework itself, rather than treating innovation policy and safety regulation as separate domains. A mandatory safety floor ensures that systems threatening national energy security remain in the highest tier regardless of innovation score. 2. Mandatory Digital Twin Testing (10,000-Hour Rule). Ultra-high-risk energy AI systems must complete 10,000 hours of digital twin simulation covering all seasons, extreme weather events, and cyberattack scenarios before real-world deployment. This approach, analogous to aviation certification standards, provides verifiable safety assurance without prohibiting deployment. 3. Four-Model Human Oversight Taxonomy. Rather than a binary human-in-the-loop requirement, our framework defines four calibrated oversight models matched to operational context, with Human-out-of-the-Loop explicitly prohibited for critical infrastructure. 4. Algorithmic Fairness Audit with Quantified Thresholds. Energy AI systems affecting consumer pricing undergo annual fairness audits measuring disparate impact ratios across income groups, with binding corrective action triggered when ratios exceed defined thresholds. 5. National AI Performance Barometer. A six-dimensional measurement system tracking digital maturity, AI effectiveness, security resilience, domestic capability, innovation ecosystem health, and equity with automatic governance review triggered when indicators decline beyond defined thresholds across consecutive years. Each mechanism is designed for adaptation by countries at comparable developmental stages.