This Lab studies AI governance and how AI reshapes regulation, ethics, culture, and public discourse across the Arab League member states, providing researchers and policymakers with evidence-based insights for the Arab world.
Anis Ben Brik's research sits at the intersection of AI governance, comparative public policy, and communication. He leads the first multi-method study of AI regulation across all Arab League member states. His work spans two major research streams:
Cross-national mapping of AI strategies, regulatory frameworks, data protection laws, and governance trajectories across MENA — integrating 14 datasets, fsQCA configurational analysis, and a five-type governance typology.
Societal dimensions of artificial intelligence including design, use, management, and policy — with particular emphasis on cultural, social, cognitive, economic, ethical, and philosophical implications. How AI reshapes identities, institutions, and power structures in diverse societal contexts.
Nine analytical dimensions of AI governance across the Arab world — from regulatory architecture and readiness to ethics, identity, and communication.
Bahrain's 38-article draft (Shura Council, Apr 2024) awaiting parliament — would be the first enacted AI law in the Arab world.
Saudi Arabia 2023. Iraq 2nd at 679 pubs with zero enacted PDPL — the research-governance paradox.
Saudi Arabia (rank #6 globally, up from #52 in 2018). UAE 0.9533 (rank 11). Yemen 0.2318 (rank 185).
Saudi Arabia ITU Global Cybersecurity Index — globally top 5. Iraq GCI 20.71 despite 82% internet penetration.
fsQCA identifies three paths to AI governance: petro-fiscal surplus, institutional pioneer, research-led emergence.
This section examines the formal architecture of AI governance across the Arab world — how states have chosen to regulate, encourage, or ignore artificial intelligence through law, policy, and institutional design. It maps the full spectrum from states with full AI ecosystems to those where no governance framework yet exists, illuminating the political, economic, and institutional drivers of regulatory divergence across Arab League members.
Zero standalone AI laws have been enacted across all Arab states. Bahrain's 38-article Draft AI Regulation Law (Shura Council approval April 2024, 7 chapters) awaits parliamentary ratification — if enacted, it would be the region's first dedicated AI legislation. All current frameworks rely on combinations of soft law, PDPL, sector-specific regulations, and existing cybercrime statutes as the backbone for AI governance.
The five-tier classification is constructed from cumulative binary indicators: AI strategy + dedicated body + enacted PDPL + risk classification + regulatory sandbox + regulation count. Kuwait represents the clearest anomaly: high-income GCC member (GDP/cap $34,076) with zero AI regulations, zero governance body, zero PDPL — the wealth-governance paradox crystallised.
| ISO | Country | Tier | Strat | Gen | Body | PM-lvl | AI Law | Draft | Risk | Sandbox | LLM | PDPL | Yr | GDPR | Regs# | Bodies# | Intl# | OECD init |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ARE | UAE | Advanced | 2nd | 2021 | 8 | 5 | 4 | 8 | ||||||||||
| SAU | Saudi Arabia | Advanced | 2nd | 2021 | 11 | 6 | 3 | 64 | ||||||||||
| QAT | Qatar | Advanced | 1st | 2016 | 8 | 4 | 3 | 0 | ||||||||||
| BHR | Bahrain | Intermediate | 1st | 2018 | 4 | 3 | 1 | 0 | ||||||||||
| EGY | Egypt | Intermediate | 2nd | 2020 | 5 | 4 | 4 | 8 | ||||||||||
| TUN | Tunisia | Intermediate | 2nd | 2004 | 6 | 3 | 3 | 7 | ||||||||||
| OMN | Oman | Emerging | 1st | 2021 | 0 | 1 | 1 | 0 | ||||||||||
| MAR | Morocco | Emerging | 1st | 2009 | 5 | 2 | 3 | 3 | ||||||||||
| IRQ | Iraq | Emerging | 1st | — | 6 | 3 | 1 | 0 | ||||||||||
| KWT | Kuwait | Minimal | — | — | — | 0 | 0 | 1 | 0 | |||||||||
| DZA | Algeria | Minimal | — | — | — | 0 | 0 | 0 | 1 | |||||||||
| JOR | Jordan | Minimal | — | Draft | — | 0 | 0 | 0 | 0 | |||||||||
| MRT | Mauritania | Minimal | 1st | — | — | 0 | 0 | 0 | 0 | |||||||||
| LBY | Libya | Absent | — | — | — | 0 | 0 | 0 | 0 | |||||||||
| LBN | Lebanon | Absent | — | — | — | 0 | 0 | 0 | 0 | |||||||||
| PSE | Palestine | Absent | — | — | — | 0 | 0 | 0 | 0 | |||||||||
| SYR | Syria | Absent | — | — | — | 0 | 0 | 0 | 0 | |||||||||
| YEM | Yemen | Absent | — | — | — | 0 | 0 | 0 | 0 | |||||||||
| SDN | Sudan | Absent | — | — | — | 0 | 0 | 0 | 0 |
This section asks how ready Arab states actually are to govern AI — and how that readiness has changed over time. It examines the multi-dimensional composite of government capability, technology sector maturity, and digital infrastructure that determines whether a state can design, implement, and enforce AI policy. Dramatic divergences between rapidly improving Gulf states and declining or stagnating Levant economies reveal how political stability, economic capacity, and institutional investment shape governance trajectories.
Saudi Arabia registers the most dramatic digital governance transformation in the region — rising from rank 52 to rank 6 globally on the UN e-Government index between 2018 and 2024, driven by sustained strategic investment in digital infrastructure and institutional capacity. Lebanon shows the starkest decline in AI readiness, with its composite score falling over 13 points as economic and political collapse eroded the institutional foundations of governance. Egypt demonstrates that non-GCC states can achieve sustained readiness improvements through deliberate national strategy and international engagement, even with severely constrained economic resources.
| ISO | Country | Tier | Oxford 2020 | Oxford 2023 | Oxford 2025 | Δ 23→25 | Δ 20→25 | EGDI 2018 | EGDI 2024 | EGDI Rank 18 | EGDI Rank 24 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ARE | UAE | Adv | 72.40 | 70.42 | 69.86 | −0.56 | −2.54 | 0.8295 | 0.9533 | 21 | 11 |
| SAU | Saudi Arabia | Adv | 56.23 | 67.04 | 71.57 | +4.53 | +15.34 | 0.7119 | 0.9602 | 52 | 6 |
| QAT | Qatar | Adv | 56.78 | 63.59 | 58.61 | −4.98 | +1.83 | 0.7132 | 0.8244 | 51 | 53 |
| BHR | Bahrain | Int | 54.75 | 56.13 | 59.57 | +3.44 | +4.82 | 0.8116 | 0.9196 | 26 | 18 |
| KWT | Kuwait | Min | 50.61 | 49.86 | 43.79 | −6.07 | −6.82 | 0.7388 | 0.7812 | 41 | 66 |
| OMN | Oman | Emg | 50.78 | 58.94 | 56.73 | −2.21 | +5.95 | 0.6846 | 0.8576 | 63 | 41 |
| EGY | Egypt | Int | 49.19 | 52.69 | 59.10 | +6.41 | +9.91 | 0.4880 | 0.6699 | 114 | 95 |
| MAR | Morocco | Emg | 36.42 | 43.34 | 43.06 | −0.28 | +6.64 | 0.5214 | 0.6841 | 110 | 90 |
| TUN | Tunisia | Int | 44.39 | 46.07 | 42.23 | −3.84 | −2.16 | 0.6254 | 0.6935 | 80 | 87 |
| DZA | Algeria | Min | 33.47 | 35.99 | 42.05 | +6.06 | +8.58 | 0.4227 | 0.5956 | 130 | 116 |
| JOR | Jordan | Min | 41.76 | 56.85 | 56.07 | −0.78 | +14.31 | 0.5575 | 0.6849 | 98 | 89 |
| LBN | Lebanon | Abs | 35.91 | 47.62 | 34.26 | −13.36 | −1.65 | 0.5530 | 0.5449 | 99 | 126 |
| IRQ | Iraq | Emg | 33.88 | 33.40 | 29.37 | −4.03 | −4.51 | 0.3376 | 0.4572 | 155 | 148 |
| LBY | Libya | Abs | 28.84* | 25.31 | 28.38 | +3.07 | −0.46 | 0.3833 | 0.5466 | 140 | 125 |
| SYR | Syria | Abs | 19.33 | 18.12 | 21.72 | +3.60 | +2.39 | 0.3459 | 0.3888 | 152 | 162 |
| YEM | Yemen | Abs | 19.07 | 19.89 | 14.48 | −5.41 | −4.59 | 0.2154 | 0.2318 | 186 | 185 |
| SDN | Sudan | Abs | 26.35 | 18.26 | 16.21 | −2.05 | −10.14 | — | — | — | — |
| MRT | Mauritania | Min | 29.42 | 27.09 | 27.58 | +0.49 | −1.84 | — | — | — | — |
| PSE | Palestine | Abs | — | 33.14 | 35.54 | +2.40 | — | — | — | — | — |
This section investigates the relationship between scientific knowledge production and governance capacity. Does a country that produces significant AI research translate that intellectual capital into effective regulation? The Arab world presents a striking paradox: several states generate substantial AI research output while their governance frameworks remain embryonic. This governance-research gap reveals that knowledge production and institutional capacity do not necessarily evolve together, challenging assumptions that underpin AI readiness rankings.
Iraq is the region's most striking governance paradox: among the highest producers of AI research in the Arab world, yet operating without a data protection law, without AI-specific legislation, and with cybersecurity scores among the lowest in the region. Algeria and Jordan present analogous patterns — substantial research communities operating entirely without governance infrastructure. These cases directly challenge the assumption that scientific capacity naturally produces regulatory capacity. The configurational analysis in Theme 05 shows that research output can partially substitute for economic resources in generating governance outcomes, but only when combined with institutional infrastructure — which none of these states currently possesses at the required level.
| ISO | Country | Tier | 2020 | 2021 | 2022 | 2023 | 2024* | R&D %GDP | Oxford 2023 | Gap Score |
|---|---|---|---|---|---|---|---|---|---|---|
| SAU | Saudi Arabia | Advanced | 685 | 793 | 829 | 1,189 | 727 | 0.56% | 67.04 | Aligned |
| IRQ | Iraq | Emerging | 432 | 559 | 643 | 679 | 448 | 0.04% | 33.40 | Critical Gap |
| EGY | Egypt | Intermediate | 470 | 521 | 617 | 669 | 393 | 1.03% | 52.69 | Moderate |
| TUN | Tunisia | Intermediate | 320 | 268 | 369 | 357 | 213 | 0.75% | 46.07 | Moderate |
| JOR | Jordan | Minimal | 242 | 263 | 293 | 281 | 218 | — | 56.85 | Gap |
| MAR | Morocco | Emerging | 162 | 184 | 202 | 234 | 144 | — | 43.34 | Moderate |
| DZA | Algeria | Minimal | 107 | 141 | 154 | 178 | 128 | — | 35.99 | Gap |
| QAT | Qatar | Advanced | 111 | 119 | 95 | 126 | 67 | 0.68% | 63.59 | Aligned |
| ARE | UAE | Advanced | — | — | — | — | — | 1.49% | 70.42 | Aligned |
| OMN | Oman | Emerging | 48 | 52 | 60 | 59 | 61 | 0.37% | 58.94 | Moderate |
| LBN | Lebanon | Absent | 29 | 27 | 28 | 24 | 18 | — | 47.62 | Collapse |
| LBY | Libya | Absent | 22 | 44 | 30 | 28 | 35 | — | 25.31 | Conflict |
| KWT | Kuwait | Minimal | 22 | 24 | 31 | 16 | 9 | 0.10% | 49.86 | Wealth Gap |
| SDN | Sudan | Absent | 18 | 30 | 23 | 20 | 7 | — | 18.26 | Conflict |
| SYR | Syria | Absent | 27 | 20 | 24 | 17 | 8 | — | 18.12 | Conflict |
| BHR | Bahrain | Intermediate | 15 | 19 | 30 | 25 | 16 | — | 56.13 | Aligned |
| PSE | Palestine | Absent | 5 | 11 | 6 | 3 | 4 | — | 33.14 | Occupation |
| MRT | Mauritania | Minimal | — | 1 | — | 1 | — | 0.01% | 27.09 | Peripheral |
| YEM | Yemen | Absent | — | — | — | — | — | — | 19.89 | Conflict |
Digital infrastructure is both a precondition for AI deployment and a dimension of governance capacity. This section maps the connectivity landscape across the Arab world — how extensively populations are connected, through what technologies, and whether infrastructure development translates into the institutional capacity needed to govern AI systems. Infrastructure paradoxes abound: states where near-universal mobile connectivity coexists with absent governance frameworks, and others where impressive telecom scores mask fragile institutional architectures.
Three infrastructure paradoxes define the Arab digital landscape. The Kuwait mobile-only paradox: among the most connected populations on earth by mobile penetration, yet with almost no fixed broadband infrastructure — an architecture uniquely vulnerable to disruption and wholly reliant on wireless networks. The Iraq infrastructure surge: internet penetration nearly tripled in five years, the fastest non-GCC growth in the region, while institutional governance capacity remained nearly static — rapid connectivity without the governance frameworks to manage its consequences. The Libya infrastructure anomaly: world-class telecom infrastructure scores sitting alongside absent state capacity — physical networks built by oil revenues but entirely disconnected from the institutions needed to govern the digital economy they enable.
| ISO | Country | Internet 2023% | Mobile/100 2023 | Broadband/100 2023 | Mobile/Broadband ratio | EGDI TII 2024 | Internet Growth 18→23 |
|---|---|---|---|---|---|---|---|
| ARE | UAE | 100.0% | 199.42 | 37.10 | 5.4× | 1.000 | +1.55pp |
| SAU | Saudi Arabia | 100.0% | 157.78 | 43.57 | 3.6× | 0.984 | +6.69pp |
| QAT | Qatar | 99.65% | 157.82 | 11.64 | 13.6× | 0.996 | 0.00 |
| BHR | Bahrain | 100.0% | 153.90 | 17.16 | 9.0× | 0.988 | +1.36pp |
| KWT | Kuwait | 99.75% | 167.68 | 1.01 | 166× | 0.999 | +0.15pp |
| OMN | Oman | 95.25% | 135.22 | 10.88 | 12.4× | 0.967 | +9.75pp |
| MAR | Morocco | 91.0% | 148.16 | 7.02 | 21.1× | 0.883 | +26.2pp |
| JOR | Jordan | 92.53% | 67.55 | 7.04 | 9.6× | 0.650 | +27.3pp |
| EGY | Egypt | 72.69% | 92.83 | 10.86 | 8.5× | 0.695 | +25.8pp |
| TUN | Tunisia | 72.35% | 134.14 | 14.14 | 9.5× | 0.836 | +8.16pp |
| DZA | Algeria | 76.91% | 111.61 | 12.01 | 9.3× | 0.813 | +27.9pp |
| IRQ | Iraq | 81.73% | 101.39 | 17.23 | 5.9× | 0.687 | +47.8pp |
| LBN | Lebanon | 83.49% | 73.93* | — | — | 0.643 | +2.59pp |
| LBY | Libya | 88.50% | 192.97* | — | — | 0.964 | — |
| PSE | Palestine | 86.64% | 76.69 | 8.37 | 9.2× | — | +22.2pp |
| SYR | Syria | — | 72.44 | 6.86 | — | 0.443 | — |
| SDN | Sudan | — | — | — | — | — | +1.84pp* |
| MRT | Mauritania | 37.38% | 90.77 | 0.59 | 154× | — | +14.4pp |
| YEM | Yemen | — | 50.89 | — | — | 0.291 | — |
AI raises profound questions about human identity, creativity, ethics, and meaning — while simultaneously reshaping how information flows, how narratives form, and how publics are constituted. This section examines AI through two complementary lenses: as a force transforming cultural expression, language, and what it means to be human in the Arab world; and as a medium and actor in communication, shaping discourse, mediating interaction, and influencing collective understanding across 22 states.
The humanities and communication sciences converge on a single underlying question: whose values, narratives, languages, and power structures are being encoded into AI systems, and who bears the consequences? In the Arab world, this question has particular urgency. Arabic is the fifth most spoken language globally yet among the most underrepresented in AI training data. Governance frameworks embed ethics from IEEE, OECD, and EU documents — frameworks designed for liberal democratic contexts with strong civil society. And AI is deployed as a communication infrastructure by states whose relationship to public discourse, press freedom, and political speech is fundamentally different from the contexts in which those systems were designed.
The data reveal a stark division: five states (UAE, Saudi Arabia, Qatar, Egypt, Tunisia) are actively building sovereign Arabic AI capabilities — large language models, domain-specific systems, cultural content infrastructure. The remaining 17 states are entirely passive, consuming AI systems designed elsewhere, encoding other languages, embedding other values.
Language is not merely a communication tool — it is the carrier of cultural memory, philosophical tradition, and social identity. When AI systems speak Arabic, translate Arabic, generate Arabic text, or make decisions affecting Arabic speakers, the question of how they represent that language becomes a question of cultural recognition and political power.
UAE frames AI as post-national and cosmopolitan. Falcon LLM released open-weights, trained on multilingual corpora. Jais (G42/MBZUAI) positions Arabic as one among many languages — not a cultural container but a technical parameter. Sovereignty posture: Active · International alignment: Western multilateral. Bletchley signatory. UNESCO referenced. EU AI Act-influenced. The UAE's governance philosophy — soft law, experimental legislation, pro-innovation — maps directly onto its identity politics: minimal cultural friction for maximum capital accumulation.
Saudi Arabia pursues the most aggressive AI sovereignty strategy in the Arab world. ALAN sovereign LLM, HUMAIN $100B AI ecosystem, data embassy model. Sovereignty posture: Aggressive. Vision 2030 explicitly frames AI as intrinsic to cultural and national renewal — Arabic language preservation, Islamic values alignment, and Saudi cultural content embedded in training data. The data protection law (PDPL 2021) and 4-tier risk classification system (mirroring EU AI Act) create a framework designed to retain data within Saudi borders while projecting cultural influence outward.
Egypt — the Arab world's largest population and its historic cultural centre — frames its National AI Strategy 2025–2030 as a pan-Arab cultural infrastructure project. Sovereignty posture: Active. First Arab and African state to join OECD.AI (2021). Domain-specific LLMs for healthcare, legal (Islamic jurisprudence), and agriculture encode culturally contextualised AI — not universal systems but Arabic-grounded ones. UNESCO and EU AI Act referenced. The Human-Centred Design principle in the strategy explicitly invokes Egyptian societal values as a design constraint.
Qatar's Fanar project (national Arabic LLM) and its AI+X paradigm represent the most explicit rejection of universal AI ethics in the dataset. AI+X insists that AI must be adapted to each sectoral and cultural context — what governance means in Islamic finance differs from what it means in healthcare or creative industries. Sovereignty posture: Moderate. The QCB's binding AI framework for financial services creates a parallel governance track: rigorous where cultural and economic stakes are highest, permissive elsewhere. The PDPPL's heightened protection for children, health, ethnicity, and religious data reflects an Islamic privacy ethics that partially aligns with GDPR but emerges from different philosophical foundations.
Tunisia presents AI governance's dark mirror. Its PDPL (2004, oldest in the Arab world) and EU GDPR-influenced framework represent genuine rights architecture. But Decree-Law 2022-54 — imposing criminal penalties for "false news via automated systems" — deploys AI governance as a tool of political censorship, criminalising algorithmic content that challenges official narratives. Tunisia's regulatory framework embodies its contested identity: democratic aspiration and authoritarian consolidation coexisting in the same legal document. EU alignment sought but democratic backsliding simultaneously enacted.
AI functions as a medium — shaping what information reaches people, how it is framed, who can speak and who is silenced. In the Arab world, where press freedom is constrained across most states, AI-driven communication systems carry particular political weight: recommendation algorithms, content moderation systems, and generative AI tools are deployed in contexts where the boundaries between governance and control are deliberately blurred.
Iraq's regulatory philosophy explicitly frames AI risk around "extremist exploitation prevention" and disinformation — positioning AI governance as information security rather than rights protection. Tunisia's Decree-Law 2022-54 criminalises "false news via automated systems." Saudi Arabia's data classification (Public/Internal/Restricted/Confidential) creates a sovereignty architecture that controls information flow as a dimension of national security. These frameworks blur the line between moderating harmful content and controlling political speech.
Regulatory sandboxes in UAE, Saudi Arabia, Qatar, and Bahrain are concentrated in financial services and healthcare — but not communication or media. This absence is significant: AI systems mediating public discourse (recommendation algorithms, chatbots, social media moderation) operate largely outside the regulatory frameworks that govern higher-profile sectors. The governance gap is widest precisely where AI's communicative influence is most pervasive.
The five states building sovereign AI capabilities (UAE, Saudi Arabia, Qatar, Egypt, Tunisia) are also those with the most developed strategies for shaping their digital public spheres. Sovereign LLMs trained on nationally curated corpora encode particular framings of history, religion, and political life. The remaining 17 states — without sovereign AI capacity and with minimal governance frameworks — have their digital public spheres shaped entirely by foreign platforms, foreign algorithms, and foreign editorial judgements.
The GCC AI Ethics Manual (2020) — the primary regional ethics framework, referenced by Bahrain, Kuwait, and Oman — draws extensively on IEEE, OECD, and EU principles designed for liberal democratic contexts with strong civil society oversight. Three states reference GCC ethics alignment in their governance frameworks. Yet the Manual's adoption was top-down, without civil society consultation, and the foundational assumptions — individual rights as the primary unit of ethical analysis, transparency as a civic entitlement, accountability to a neutral legal system — map imperfectly onto Gulf political structures, Islamic jurisprudence, and tribal-familial social organisation.
A more contextually grounded ethics might draw on maqāṣid al-sharīʿa — the Islamic jurisprudential framework of objectives: preservation of life, intellect, lineage, wealth, and religion. These provide resources for AI ethics that Western bioethics cannot readily supply: a language of communal obligation, of intergenerational responsibility, of the relationship between knowledge and accountability that does not reduce to individual consent and data rights. Qatar's AI+X paradigm gestures toward this — but no Arab state has yet articulated a fully indigenous AI ethics framework grounded in the region's own philosophical traditions.
Ben Brik's accountability dispersal thesis (forthcoming, Oxford University Press) argues that algorithmic governance in MENA concentrates coercive capacity while dispersing accountability — creating state power simultaneously more capable and less responsible than any previous modality. In communication terms: AI systems can monitor, filter, amplify, and suppress speech at scale, but the question of who is responsible for those effects is dispersed across algorithms, foreign vendors, national security statutes, and regulatory lacunae.
Where accountability is most dispersed — Libya, Yemen, Syria, Palestine — AI communication systems operate in a complete governance vacuum. Where it is most concentrated — UAE, Saudi Arabia — formal mechanisms exist but oversight remains captured by the state apparatus whose communication practices those mechanisms are meant to govern.
Beyond statistical indices lies the question of how AI governance is actually organised as a political and institutional practice. This section classifies Arab states into distinct governance archetypes — examining the structure of authority, the philosophy of regulation, the mechanisms of enforcement, and the stance each state takes toward AI as a sovereign, national, or technocratic project. Eight clusters emerge, from Gulf innovation leaders pursuing aggressive AI sovereignty strategies to fragmented states where governance has collapsed entirely.
| ISO | Country | Governance Type | Regulatory Approach | Enforcement Model | Sovereignty Posture | Intl. Alignment | Cluster |
|---|---|---|---|---|---|---|---|
| ARE | UAE | Centralized-Federal | Soft law + experimental legislation | Indirect (procurement) | Active (Falcon, MBZUAI) | Western multilateral | Gulf Innovation Leader |
| SAU | Saudi Arabia | Centralized-PM level | Hybrid (PDPL hard + soft AI) | Direct (SDAIA fines) | Aggressive (ALAN, embassies) | Selective multilateral | Gulf Sovereignty Pioneer |
| QAT | Qatar | Centralized-PM level | Hybrid (ethics + binding finance) | Sectoral (QCB binding) | Moderate (Fanar, QIA) | Regional (GCC+DCO) | Gulf Sectoral Regulator |
| BHR | Bahrain | Ministerial | Licensing-based (proposed AI law) | Proposed (3yr + fines) | Aspirational | GCC-oriented | Gulf Early Legislator |
| KWT | Kuwait | None | None | None | Passive | None | Gulf Governance Absent |
| OMN | Oman | Fragmented | Strategic vision only | None (draft) | Aspirational | GCC-oriented | Gulf Emerging |
| EGY | Egypt | Centralized-Ministerial | Phased soft-to-hard | Supervisory (MCIT) | Active (national FM) | OECD adherent | MENA Institutional Leader |
| MAR | Morocco | Multi-stakeholder | Horizontal framework law | Graduated sanctions | Moderate | EU+UNESCO | North Africa Digital Hub |
| TUN | Tunisia | PM-level council | Hybrid (hard law + sandboxes) | Bifurcated (INPDP+DL54) | Aspirational | EU-influenced | North Africa Innovation Bridge |
| DZA | Algeria | None | None | None | Passive | None | North Africa Governance Absent |
| JOR | Jordan | None | None | None | Passive | None | Levant Passive |
| IRQ | Iraq | Centralized-PM level | Agile governance + sectoral sandbox | No AI penalties (existing) | Aspirational (AICTO) | UNDP-supported | Post-Conflict Emerging |
| LBN | Lebanon | None | None | None | Passive | None | Conflict-Affected Absent |
| PSE | Palestine | None | None | None | Passive | None | Conflict-Affected Absent |
| LBY | Libya | None | None | None | Passive | None | Conflict-Affected Absent |
| SYR | Syria | None | None | None | Passive | None | Conflict-Affected Absent |
| YEM | Yemen | None | None | None | Passive | None | Conflict-Affected Absent |
| SDN | Sudan | None | None | None | Passive | None | Conflict-Affected Absent |
| MRT | Mauritania | None | Strategic vision only | None | Passive | None | Peripheral Minimal |
AI governance requires people — researchers, regulators, engineers, civil servants, educators — with the knowledge and skills to design, implement, and oversee AI systems. This section examines the human capital foundations of Arab AI governance: the education infrastructure, skills ecosystems, and talent trajectories that determine whether states can staff the institutions their governance frameworks require. Where human capital is declining, governance ambitions will remain aspirational regardless of how many strategies are formally adopted.
Human capital is the bridge between governance aspiration and governance reality. A national AI strategy means little without people capable of implementing it; a regulatory framework is empty without civil servants, judges, and technologists who can interpret and enforce it. Saudi Arabia's dramatic rise up the global e-government rankings reflects not just infrastructure investment but sustained investment in education and skills. Conversely, states experiencing human capital decline — through emigration, conflict, or economic collapse — find their governance ambitions systematically undermined at the implementation level. The Arab region's human capital landscape is sharply divided, and that division closely predicts which governance strategies will be realised and which will remain aspirational documents.y attributable to HCI investment: 0.8101 (2018) → 0.9067 (2024). Yemen's HCI = 0.267 (2024, rank 185) represents the most acute human capital deficit — AI governance is structurally impossible without the human infrastructure to staff, implement, and oversee regulatory bodies.
The fsQCA state capacity condition (C1) integrates HCI as a component: full membership requires EGDI ≥ 0.85 AND n_governance_bodies ≥ 4. Countries with strong HCI but weak institutional infrastructure (Jordan HCI 0.6458, institutions=0) demonstrate that human capital alone cannot produce governance without the parallel institutional architecture.
| ISO | Country | Tier | HCI 2018 | HCI 2020 | HCI 2022 | HCI 2024 | Δ 18→24 | OSI 2024 | e-Partic. 2024 | fsQCA State Cap. |
|---|---|---|---|---|---|---|---|---|---|---|
| ARE | UAE | Adv | 0.6877 | 0.7320 | 0.8711 | 0.9436 | +0.256 | 0.9163 | 0.781 | 0.95 |
| SAU | Saudi Arabia | Adv | 0.8101 | 0.8648 | 0.8662 | 0.9067 | +0.097 | 0.9900 | 0.959 | 0.90 |
| QAT | Qatar | Adv | 0.6683 | 0.6698 | 0.7150 | 0.7114 | +0.043 | 0.7655 | 0.480 | 0.85 |
| BHR | Bahrain | Int | 0.7897 | 0.8439 | 0.8154 | 0.8680 | +0.078 | 0.9031 | 0.904 | 0.80 |
| KWT | Kuwait | Min | 0.6852 | 0.7470 | 0.7706 | 0.7083 | +0.023 | 0.6366 | 0.301 | 0.65 |
| OMN | Oman | Emg | 0.7013 | 0.7751 | 0.8067 | 0.7977 | +0.096 | 0.8077 | 0.658 | 0.70 |
| EGY | Egypt | Int | 0.6072 | 0.6192 | 0.6375 | 0.6150 | +0.008 | 0.7002 | 0.589 | 0.60 |
| MAR | Morocco | Emg | 0.5278 | 0.6152 | 0.6350 | 0.6078 | +0.080 | 0.5618 | 0.438 | 0.55 |
| TUN | Tunisia | Int | 0.6640 | 0.6974 | 0.6911 | 0.6497 | −0.014 | 0.5951 | 0.452 | 0.55 |
| DZA | Algeria | Min | 0.6640 | 0.6966 | 0.6956 | 0.6418 | −0.022 | 0.3320 | 0.055 | 0.35 |
| JOR | Jordan | Min | 0.7387 | 0.6800 | 0.6967 | 0.6458 | −0.093 | 0.7591 | 0.616 | 0.55 |
| LBN | Lebanon | Abs | 0.6649 | 0.6567 | 0.6656 | 0.5433 | −0.121 | 0.4489 | 0.466 | 0.35 |
| IRQ | Iraq | Emg | 0.5094 | 0.4358 | 0.5888 | 0.4967 | −0.013 | 0.1876 | 0.096 | 0.30 |
| LBY | Libya | Abs | 0.7173 | 0.7357 | 0.7534 | 0.5952 | −0.122 | 0.0808 | 0.014 | 0.15 |
| SYR | Syria | Abs | 0.4860 | 0.5073 | 0.4983 | 0.4169 | −0.069 | 0.3068 | 0.069 | 0.05 |
| YEM | Yemen | Abs | 0.4037 | 0.4142 | 0.3633 | 0.2670 | −0.137 | 0.1377 | 0.151 | 0.05 |
Cybersecurity capacity is a critical dimension of the broader AI governance challenge. States that cannot protect their digital infrastructure, respond to threats, or establish trusted legal frameworks for data security lack a foundational prerequisite for responsible AI deployment. This section maps the cybersecurity landscape across the Arab world through multiple complementary measurement frameworks, revealing stark asymmetries between globally competitive Gulf states and populations that remain highly exposed to cyber threats with minimal institutional protection.
Cybersecurity capacity and AI governance capacity are deeply interrelated but not identical. The Gulf states demonstrate that deliberate national investment can build world-class cybersecurity frameworks rapidly, achieving global recognition for legal, technical, and organisational preparedness. Yet several Arab states with moderate governance ambitions show significant cybersecurity vulnerabilities, and populations that are heavily connected digitally in some of the conflict-affected states face high exposure to cyber threats with minimal institutional protection — a compounding vulnerability that makes responsible AI deployment effectively impossible.l AI governance and low cybersecurity capacity. The CEI (Cyber Exposure Index) reveals a counterintuitive pattern: states with zero AI governance (Palestine CEI=0.855, Libya CEI=0.793, Morocco CEI=0.748) score highest on cyber exposure — meaning they are most affected by cybersecurity incidents with the least capacity to respond.
| ISO | Country | Tier | GCI (0–100) | NCSI (0–100) | DDL | CEI (0–1) | Internet 2023% |
|---|---|---|---|---|---|---|---|
| SAU | Saudi Arabia | Advanced | 99.54 | 84.42 | 63.89 | 0.39 | 100% |
| ARE | UAE | Advanced | 98.06 | 40.26 | 68.87 | 0.359 | 100% |
| OMN | Oman | Emerging | 96.04 | 45.45 | 59.51 | — | 95.25% |
| EGY | Egypt | Intermediate | 95.48 | 57.14 | 46.93 | 0.548 | 72.69% |
| QAT | Qatar | Advanced | 94.50 | 58.44 | 64.99 | 0.241 | 99.65% |
| TUN | Tunisia | Intermediate | 86.23 | 53.25 | 46.26 | 0.614 | 72.35% |
| MAR | Morocco | Emerging | 82.41 | 70.13 | 46.88 | 0.748 | 91.0% |
| BHR | Bahrain | Intermediate | 77.86 | 25.97 | 65.17 | — | 100% |
| KWT | Kuwait | Minimal | 75.07 | — | — | 0.428 | 99.75% |
| JOR | Jordan | Minimal | 70.96 | 28.57 | 54.07 | 0.586 | 92.53% |
| SDN | Sudan | Absent | 35.03 | 11.69 | 25.50 | — | — |
| DZA | Algeria | Minimal | 33.95 | 33.77 | 42.81 | 0.721 | 76.91% |
| LBN | Lebanon | Absent | 30.44 | — | — | 0.579 | 83.49% |
| LBY | Libya | Absent | 28.78 | 10.39 | 41.10 | 0.793 | 88.5% |
| PSE | Palestine | Absent | 25.18 | — | — | 0.855 | 86.64% |
| SYR | Syria | Absent | 22.14 | 15.58 | 33.40 | — | — |
| IRQ | Iraq | Emerging | 20.71 | 5.19 | — | 0.690 | 81.73% |
| MRT | Mauritania | Minimal | 18.94 | 11.69 | 11.30 | — | 37.38% |
| YEM | Yemen | Absent | — | 7.79 | — | — | — |
Each of the Arab League member states has a distinct AI governance story shaped by its political system, economic resources, institutional history, and geopolitical position. This section presents individual state profiles that bring together the full cross-sectional evidence — regulatory frameworks, readiness trajectories, research output, infrastructure, cybersecurity capacity, and governance typology — into a single comparable portrait, making visible both the diversity of governance paths across the region and the structural factors that constrain or enable each state's trajectory.
This Lab studies AI governance and society, examining how AI reshapes regulation, institutional frameworks, human identity, creativity, ethics, discourse, and public opinion across all Arab League member states. It maps regulatory landscapes, analyses governance configurations, examines AI sovereignty strategies, tracks readiness trajectories, and provides researchers and policymakers with rigorous evidence and policy recommendations to shape the Arab world's AI governance and societal future.
Standard regression analysis assumes symmetry (high X causes high Y, and low X causes low Y) and additivity (each condition contributes independently to the outcome). Both assumptions are empirically untenable at N=22. The fsQCA approach treats governance as a configurational outcome — produced by specific combinations of conditions, with multiple paths to the same outcome (equifinality) and asymmetric causality.
Algorithmic governance in MENA does not simply strengthen authoritarian control — it fundamentally transforms how power operates by concentrating coercive capacity while dispersing accountability, creating a form of state power that is simultaneously more capable and less responsible. This is the central theoretical contribution of The Algorithmic State in the MENA Region (Oxford University Press, forthcoming).
Petro-fiscal surplus pathway (UAE, SAU, QAT): high economic resources + high state capacity + high digital infrastructure → Advanced governance. Institutional pioneer pathway (EGY, TUN): high data protection + high international alignment + high research output, without high economic resources → Intermediate/Advanced governance without wealth. Research-led emergence pathway (IRQ): high research output without state capacity or data protection → partial governance (outcome 0.45), but the research-governance gap prevents full emergence.
Multi-method design integrating star-schema database architecture, fuzzy-set Qualitative Comparative Analysis (fsQCA), Oxford Insights and UN EGDI panels, WDI economic indicators, and qualitative regulatory profiling across Arab League member states.
Fourteen datasets are organised in a star schema with F1_Country_Cross_Section (N=22, 46 variables) as the central fact table. Dimension tables extend by year (F2, F3, F5, F6, F8), by analytical method (C1, C2), and by qualitative profiling (D1, M3). All tables share the iso3 primary key, enabling full merge at any level of analysis.
| Table | N (rows) | Variables | Type | Key Variables | Source |
|---|---|---|---|---|---|
F1 | 22 | 46 | Cross-section | has_ai_strategy, n_ai_regulations, regulatory_maturity_tier | Regulations.AI, OECD.AI, manual coding |
F2 | 101 | 9 | Country-year panel | ai_pubs, oxford_ai_score, egdi_score | OECD.AI/Scopus, Oxford Insights, UN DESA |
F3 | 132 | 9 | WDI panel | internet_users_pct, gdp_per_capita_usd, rd_expenditure_gdp_pct | World Bank WDI |
F4 | 22 | 8 | Cross-section | gdp_pc_2020–2023, income_group, fuzzy_economic_resources | World Bank + manual fuzzy calibration |
F5 | 137 | 7 | Oxford panel 2020–2025 | overall_score, pillar_government, pillar_tech_sector, pillar_data_infra | Oxford Insights (CC BY-SA 4.0) |
F6 | 64 | 9 | EGDI biennial 2018–2024 | egdi_rank, egdi_score, osi, hci, tii, eparticipation | UN DESA EGDI 2018–2024 |
F7 | 22 | 6 | Cross-section | gci_score, ncsi_score, ddl_score, cei_score | ITU, e-Governance Academy, Comparitech |
F8 | 176 | 21 | Master panel (merged) | All F-table variables merged | Merge of F2–F7 |
C1 | 22 | 10 | fsQCA fuzzy sets | OUT_ai_governance_quality + 7 COND_ variables | C2 calibration anchors, manual coding |
C2 | 8 | 7 | Calibration anchors | full_in/crossover/full_out thresholds per set | Ragin (2008) direct calibration |
D1 | 21 | 4 | Qualitative profiles | regulatory_philosophy, enforcement_mechanisms, data_sovereignty_approach | Manual coding from primary documents |
M3 | 22 | 8 | Typology matrix | governance_type, regulatory_approach_type, enforcement_model, cluster_label | Manual coding from D1 + F1 |
Z1 | 26 | 7 | Codebook | Variable definitions, sources, calibration notes | Internal |
Fuzzy-set Qualitative Comparative Analysis (Ragin 2008; Schneider & Wagemann 2012) is appropriate for small-N, medium-N analysis where the goal is to identify causal configurations rather than net effects. With N=22, standard OLS regression produces unreliable estimates. fsQCA converts continuous variables to fuzzy set membership scores (0 = fully out, 0.5 = crossover, 1 = fully in) using direct calibration against theoretically-grounded anchors from C2_Calibration_Anchors.
The analysis identifies three causal pathways and reports both necessity analysis (which conditions are consistently present when the outcome is present) and sufficiency analysis (which configurations, alone or in combination, produce the outcome). The coverage and consistency statistics for each solution are reported with the full fsQCA output.
The full dataset supporting this research is publicly available on the Harvard Dataverse as an open-access repository:
The repository includes all 14 CSV datasets (F1–F8, C1–C2, D1, M3, Z1, Arab_AI_ANALYTICAL.xlsx, Arab_AI_Source_Archive.xlsx), codebook, calibration anchors, and full variable documentation. Data are released under CC BY 4.0 for academic and policy use.
Three forthcoming monographs and a programme of journal articles and working papers emerging from the Arab AI Governance Lab research.
Please use the following citations when referencing the Arab AI Governance Lab research programme or dataset.
The Arab AI Governance Lab is directed by Prof. Anis Ben Brik (USI Switzerland / UC Berkeley) and brings together co-researchers from Jagiellonian University and UC Berkeley, and research assistants from UC Berkeley and Cornell University.
Prof. Anis Ben Brik is Associate Professor at the Faculty of Communication, Culture and Society, Institute of Communication and Public Policy, Università della Svizzera italiana (USI), Switzerland, and Visiting Professor at UC Berkeley. His research programme sits at the intersection of comparative politics, digital governance, and Middle Eastern and North African studies. He is the leading scholarly authority on AI governance in the Arab world, directing the first multi-method study of AI regulatory frameworks, readiness trajectories, and governance configurations across all Arab League member states.
His research spans two major streams: Arab AI Governance — the first multi-method study of AI regulation across Arab League states — and AI & Society, examining the societal implications of AI including design, use, management, and policy, with particular emphasis on cultural, social, cognitive, economic, ethical, and philosophical dimensions of how AI transforms human experience. His forthcoming monographs with Oxford University Press (The Algorithmic State in the MENA Region) and NYU Press (Governing the Machine: Democracy, Power, and the Regulation of Artificial Intelligence) make original contributions to comparative AI governance, configurational analysis, and the accountability dispersal thesis — the proposition that algorithmic governance concentrates coercive capacity while dispersing accountability in ways without precedent in authoritarian governance.
Dr Magdalena Pycińska is a researcher at the Institute of the Middle and Far East (Instytut Bliskiego i Dalekiego Wschodu) at Jagiellonian University, Kraków (ul. Oleandry 2a, 30-063 Kraków). Her expertise spans Middle Eastern area studies, regional politics, and digital governance in the MENA context — providing the project with deep regional and cultural knowledge essential to interpreting the dataset's qualitative dimensions.
orient.uj.edu.pl →Professor Neil Gilbert holds the Milton and Gertrude Chernin Chair in Social Welfare and Social Services at the University of California, Berkeley. One of the foremost scholars of comparative social welfare policy, his work on welfare state transformation, social services, and the political economy of care informs the project's analysis of how AI governance intersects with social protection frameworks across the Arab world.
socialwelfare.berkeley.edu →Political scientist and international affairs analyst specialising in Middle Eastern issues. His research focuses on authoritarian states, systemic transformation, foreign policies of West Asian states, state reconstruction, and strategic problems. He has conducted extensive field research across the Middle East and North Africa — including Syria, Jordan, Lebanon, and Iraq — combining theoretical reflection with participatory observation. He was a Visiting Scholar at the American University of Beirut (2011/12) and Associate Professor at University Utara Malaysia (2014–15). He has delivered guest lectures in the Netherlands, Algeria, the US, and Iraq, and comments on international affairs for Polish and foreign media.
Jean-Patrick Villeneuve holds a Ph.D. from IDHEAP/HEC Lausanne, an M.Phil. from Cambridge University, an M.A/M.P.A. from Concordia University, and a B.A. from McGill University. His research focuses on four interconnected topics: anti-corruption, civic participation, transparency, and accountability — examining their measurement, policy challenges, and real-world impact. He is Co-Director of the Master programme in Public Management and Policy (offered in collaboration with the University of Lausanne and the University of Berne). His work has been funded by the European Science Foundation, Swiss National Science Foundation, and the Social Sciences and Humanities Research Council of Canada. He has published in leading academic journals and with top publishing houses, and serves on multiple journal boards and academic associations.