Episode Transcript
[00:00:00] Saudi Arabia's effort to align language, education and workforce development by Nuhar Aleji as artificial intelligence becomes embedded within communication systems, it increasingly informs how content is conceived, structured, and framed. From the outset, translation is no longer confined to the final stage of content production, nor to human deliberation.
[00:00:26] It is increasingly shaped by the architecture of the systems through which language moves.
[00:00:32] In this context, AI enabled translation refers to not only machine translation engines, but also the broader systems that shape how language is produced, evaluated, and governed at scale. At this point, the question becomes whether the construction, validation and circulation of meaning remain anchored in human judgment.
[00:00:55] After all, every translation system operates through choices, some explicit and many embedded beneath the surface.
[00:01:03] Decisions about equivalence, emphasis, simplification and omission accumulate over time.
[00:01:10] Eventually they stop appearing as decisions at all and begin to function as defaults.
[00:01:16] Meaning moves faster, while reflection on who defines it recedes.
[00:01:21] Globally, AI has expanded translation capacity at a remarkable pace. What has not advanced at the same speed is governance. While automation has increased reach, responsibility has become diffuse. When meaning breaks down, it is often unclear where authority resides or how it should have been designed in the first place.
[00:01:43] This gap between technical capability and institutional responsibility is where questions of policy, stewardship and system design begin to converge.
[00:01:53] Against this backdrop, Saudi Arabia approaches AI in translation not as a matter of adoption, but as a question of design.
[00:02:02] Language is considered alongside governance, infrastructure, talent development, and research capacity.
[00:02:10] This is not a rhetorical distinction. It affects where authority sits and how accountability is distributed.
[00:02:17] AI stewardship and risks this orientation is reflected in the country's broader AI goals.
[00:02:25] Since the establishment of the Saudi data and AI authority in 2019, AI has been framed as a national capability rather than a standalone technology.
[00:02:37] Institutional coordination, ethical frameworks, and human capital development have advanced alongside deployment.
[00:02:45] Progress may appear gradual, but it has been intentional. As with any system level transformation, outcomes are uneven and still evolving. But the underlying architecture has been designed with long term stewardship in mind. For language, that intentionality matters. The Saudi concern is not primarily how to automate translation more efficiently, but rather how translation systems should be structured from the outset.
[00:03:13] Where standards are set, how outcomes are evaluated, which dimensions of meaning require human judgment, and how linguistic priorities are preserved as systems scale.
[00:03:24] Saudi Arabia recognizes that Arabic is not merely a medium of communication. It carries legal authority, religious interpretation, historical continuity, and social legitimacy.
[00:03:37] When AI mediates translation at scale, the central question is not whether Arabic can be processed, but how its meanings are interpreted, constrained, and authorized. The global development of AI highlights the risk.
[00:03:51] Many language models are shaped primarily by English dominant data environments for languages such as Arabic, with layered registers and deep cultural anchoring. This imbalance can produce translations that are technically fluent yet contextually thin.
[00:04:08] The loss is gradual and rarely visible. At first, Saudi Arabia's response has been the deliberate reduction of ambiguity, innovations and initiatives across the kingdom. Translation is increasingly understood as shared infrastructure. When language systems are framed this way, priorities shift, human capability becomes foundational and quality is designed into workflows rather than inspected at the end.
[00:04:38] In practice, this has begun to reshape workflows across media and public communication, where translation is increasingly integrated earlier in content development cycles, with human editors overseeing AI outputs as part of standard editorial processes rather than post hoc correction. This becomes most visible where misinterpretation carries real consequences.
[00:05:03] During recent Hajj seasons, AI enabled translation systems supported multilingual access at unprecedented scale.
[00:05:11] Speed and scale were essential, yet neither displaced human authority over meaning.
[00:05:17] Automated systems absorbed volume, while human oversight remained integral to meaning sensitive decisions. What distinguished these deployments was not the technology itself, but the structure governing it.
[00:05:30] The same logic underpins Saudi Arabia's investment in national AI infrastructure through entities such as Humane, a full stack AI company operating within a nationally governed ecosystem. Development is approached as an integrated system spanning compute infrastructure, foundation models, applied platforms, and deployment standards for translation. This means decisions about data, language priorities and system behavior are no longer incidental they are structurally visible and accountable. Arabic first development follows naturally from this logic. Efforts such as alm, an Arabic first Large Language model LLM trained on hundreds of billions of Arabic words across diverse linguistic domains, reflect a move away from adapting global systems after the fact toward building language intelligence with Arabic as a design priority.
[00:06:27] Governing AI in translation.
[00:06:31] In many AI translation environments, human intervention occurs only when systems fail. Authority collapses into exception Handling Saudi Arabia's approach resists this dynamic.
[00:06:44] Human expertise defines intent, sets standards, and evaluates meaning continuously. Friction is accepted where necessary. Speed is valued but never allowed to override judgment. For linguists, value increasingly concentrates in evaluation, contextual judgment, domain specific reasoning, and ethical decision making. These are system critical roles in an AI mediated language economy.
[00:07:12] Taken together, these shifts signal a broader transformation. As AI matures, translation begins to function less as an industry and more as an economy.
[00:07:24] Value accrues around the structures that govern it, evaluation frameworks, professional certification, ethical localization practices, and advanced training pathways.
[00:07:35] Saudi Arabia is positioning itself deliberately within this landscape by aligning governance, research, capability building, and deployment. Under a shared strategic logic, translation is embedded within the national AI ecosystem rather than treated as an external dependency. The future of AI and translation will not be determined by throughput alone, but rather by who defines how systems operate, who remains accountable as they scale, and whose languages are allowed to shape meaning in a computational world.
[00:08:10] Saudi Arabia has chosen to engage that question at the level where it matters most, by design.
[00:08:16] This article was written by Nuha Aleji. She is director of Academic Collaboration and Partnerships at the Saudi Data and AI Authority.
[00:08:25] She helps advance national AI education and capability building. Integrating linguistic dimensions to support multilingual priorities and strategic transformation.
[00:08:37] Originally published in Multilingual Magazine, Issue 248 January 2026.
