Turkmenistan<\/strong> has university AI programme components, confirmed by the GIRAI assessment. What is absent is the full institutional architecture that would give those programmes national consequence: no academic publishing on AI, no expert participation in regional or international forums, no research institution with the infrastructure or mandate to translate AI work into policy dialogue. The components exist in isolation from each other and from any governance process.<\/p>\n\n\n\nInvestments: Where the Money Is Coming From<\/h2>\n\n\n\n
Across Central Asia, AI investment is primarily state-driven. Private sector and international donor investment is present in each country but plays a secondary role to government commitment \u2014 which means the direction of AI capacity is shaped more by state priorities than by market demand or civil society input.<\/p>\n\n\n\n
Kazakhstan’s investment model is the most explicitly articulated. The 2023 Concept on Digital Transformation, Development of ICT, and Cybersecurity Industry commits to creating a national AI platform by December 2024, with a roadmap for AI development completed by December 2023. The National Development Plan until 2029 \u2014 with its headline target of GDP doubling to $450 billion \u2014 names AI as a tool for healthcare diagnostics specifically. The investment is real and the timelines are specific. The gap the GIRAI researcher identifies is between the scale of the commitment and the thinness of the governance framework surrounding it: frameworks for algorithmic accountability, human oversight, and transparency are underdeveloped relative to the pace of implementation commitments.<\/p>\n\n\n\n
Tajikistan’s investment model is multi-source: state budget, development partners, and private sector. The zypl.ai commercial investment is self-sustaining \u2014 the company generates revenue from its financial services AI \u2014 which makes Tajikistan unusual in having a commercial AI actor that is not dependent on state subsidy. The broader strategy targets AI-related activity at 5% of GDP by 2040 and 1% by 2026; the investment pathway to those targets is not yet fully specified.<\/p>\n\n\n\n
Uzbekistan’s investment in AI flows primarily through the 2021 presidential programme’s identified pilot sectors \u2014 finance, banking, tax, e-government \u2014 and the IT Park ecosystem that supports private sector technology activity. The World Bank’s November 2023 Country Climate and Development Report frames a complicating context: rapid population and economic growth will drive significant increases in emissions, placing strain on natural resources. The energy implications of AI infrastructure investment have not yet entered the responsible AI conversation in Uzbekistan.<\/p>\n\n\n\n
Kyrgyzstan’s AI investment is smaller in absolute scale than Kazakhstan’s or Uzbekistan’s, but notable for its specificity: the binding national language policy documents commit government resources specifically to AI for the Kyrgyz language, which means the investment is oriented toward a rights-adjacent outcome (linguistic preservation and inclusion) rather than purely toward economic efficiency. This is unusual in the regional context.<\/p>\n\n\n\n
Competencies: Skills, Engagement, and the Ethics Gap<\/h2>\n\n\n\n
The National Responsible AI Capacities dimension asks not only whether a country has AI research institutions and investment, but whether the people and organizations responsible for AI governance have the competencies to govern it responsibly. Across Central Asia, this is where the assessment finds the most consistent weakness.<\/p>\n\n\n\n
Kazakhstan’s AI Concept acknowledges the competency gap at the baseline: «many citizens lack understanding of how these technologies function across sectors.»<\/em> This is a candid admission in a strategy document \u2014 and one that sits awkwardly alongside the two-week public consultation window for that same document. The GIRAI researcher flags this as a question about genuine public engagement rather than formal compliance: a consultation designed to satisfy a procedural requirement, not to draw on citizen knowledge that the document itself acknowledges is currently insufficient.<\/p>\n\n\n\nTajikistan’s AI Council has acknowledged, in conversations with the GIRAI researcher, that members of parliament lack the knowledge required to formulate AI standards or regulatory policy. The gap is not just between strategy documents and practice; it runs through the institutions responsible for closing it. The AI Council’s own composition \u2014 centred on Azimi and zypl.ai \u2014 means that the entity responsible for AI governance is also the country’s primary commercial AI actor. The conflict between development promotion and accountability oversight is unresolved.<\/p>\n\n\n\n
Uzbekistan’s competency picture is the hardest to read. The GIRAI researcher found low involvement of civil society organizations and academic institutions in AI policy dialogue \u2014 their engagement identified as crucial for fostering innovation, providing expert insight, and ensuring ethical development, but mechanisms to integrate them into the national AI strategy were not yet in place during the research period. Private sector engagement was similarly difficult to document: data on private sector AI activities was sparse, making it hard to assess the full extent of non-government engagement.<\/p>\n\n\n\n
Kyrgyzstan’s competency story runs through the organizations that engaged with the GIRAI assessment process itself: the academic institutions working on AI and NLP for the national language, the civil society actors covering algorithm effects in media and economic reporting, and the government agencies implementing binding policy commitments on AI in language and education. These are not AI governance specialists in the narrow sense; they are actors with specific knowledge of specific domains where AI intersects with their work. That distributed competency is what the GIRAI assessment captured and confirmed.<\/p>\n\n\n\n
What the Dimension Shows<\/h2>\n\n\n\n
The National Responsible AI Capacities dimension is where the regional differences are most quantifiable \u2014 in number of research institutions, in scale of government commitment, in commercial AI deployment. It is also the dimension where the relationship between capacity and responsible governance is most fraught. A country can have twenty-four AI research institutions and a two-week public consultation window. A country can have a commercially dominant AI loan-underwriting firm and no accountability framework for algorithmic credit decisions. The GIRAI assessment captures both the capacity and the gap. The question subsequent assessments will be able to answer is whether they move together.<\/p>\n\n\n\n
\n\n\n\nBased on the GIRAI 1st Edition (2023) National Responsible AI Capacities dimension findings for the five Central Asian states. Data source: global-index.ai<\/a>. Regional hub: IDFI (Georgia). Kyrgyzstan country researcher: Aziz Soltobaev. This is a regional thematic comparison by KG Labs.<\/p>\n\n","protected":false},"excerpt":{"rendered":"National AI Capacities Across Central Asia: What the GIRAI 2023 Assessment Found The Global Index on Responsible AI (GIRAI) 1st Edition evaluated national AI capacities across three sub-dimensions: institutions, investments, and competencies. Across Central Asia, the National Responsible AI Capacities dimension is where the regional differences are sharpest \u2014 in institutional depth, workforce pipeline, commercial […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[246,357],"tags":[523,522,517,484,644,367,524,521,497,637,673,485,506],"class_list":["post-7794","post","type-post","status-publish","format-standard","hentry","category-ai-governance","category-research-and-evidence","tag-ai-institutions","tag-ai-research","tag-central-asia-comparison","tag-girai","tag-series-girai-2023","tag-it-park","tag-kyrgyz-language-nlp","tag-national-ai-capacities","tag-nazarbayev-university","tag-format-research","tag-op-research-evidence","tag-responsible-ai","tag-zypl-ai"],"translation":{"provider":"WPGlobus","version":"3.0.2","language":"ru","enabled_languages":["en","ru"],"languages":{"en":{"title":true,"content":true,"excerpt":false},"ru":{"title":false,"content":false,"excerpt":false}}},"_links":{"self":[{"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/posts\/7794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/comments?post=7794"}],"version-history":[{"count":1,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/posts\/7794\/revisions"}],"predecessor-version":[{"id":7833,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/posts\/7794\/revisions\/7833"}],"wp:attachment":[{"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/media?parent=7794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/categories?post=7794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kglabs.org\/ru\/wp-json\/wp\/v2\/tags?post=7794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}