Introduction

The rapid expansion of artificial intelligence across media industries has transformed the conditions under which translation is produced, circulated, and evaluated. Translation is no longer situated exclusively at the end of a textual chain as a secondary operation applied to an already stabilized message. In digital and platformized environments, it increasingly becomes part of a continuous editorial process in which textual drafting, metadata generation, subtitling, automated suggestion, revision, and publication are interwoven. This transformation affects not only tools and workflows, but the very definition of what counts as translation in contemporary media ecologies.

In such environments, the specialized translator occupies a particularly sensitive position. On the one hand, institutional and commercial actors frequently imagine AI as a solution to the economic and temporal pressures that shape multilingual communication. On the other hand, the circulation of news, audiovisual content, branded narratives, and public information across linguistic spaces continues to depend on acts of contextualization that exceed probabilistic matching. The translator therefore remains indispensable precisely where discourse becomes culturally saturated, politically exposed, or pragmatically unstable.

This article argues that the integration of AI into media translation should not be read through the reductive opposition between human replacement and human preservation. A more productive approach is to examine how automation reorganizes the distribution of translational labour and how it relocates value within professional practice. In many settings, what is displaced is less the translator as such than a narrow conception of translation as lexical transfer. What gains prominence, by contrast, is the translator’s capacity to arbitrate between linguistic possibilities, discursive norms, institutional intentions, and the interpretive expectations of diverse audiences.

Such a perspective resonates with a broad body of work in translation studies that has already demonstrated that translation is embedded in social, technological, and ideological structures rather than reducible to a simple relation of equivalence. Media translation, in particular, has long shown that textual transfer in journalism, audiovisual environments, and digital interfaces is constrained by genre, speed, multimodality, editorial priorities, and asymmetries of visibility (Bielsa & Bassnett, 2009; Pérez-González, 2014). The arrival of AI does not suspend these constraints; it renders them more acute by compressing time, multiplying outputs, and normalizing procedural opacity.

The notion of intercultural mediation provides a useful conceptual framework for understanding this reconfiguration. To translate in media environments is not merely to select lexical substitutes across languages. It is to negotiate frames of intelligibility, modulate pragmatic force, preserve or redistribute emphasis, anticipate possible misreadings, and sometimes resist simplifications generated by industrial scale. In this sense, the translator is situated at the intersection of language, discourse, editorial institution, and audience reception. This mediating position becomes even more decisive when AI systems generate fluent outputs that may appear correct while remaining contextually or ethically inadequate.

It is also important to stress the adjective specialized in the phrase specialized translator. Specialization does not refer only to terminology or domain-specific vocabulary. In media environments, specialization often implies familiarity with editorial genres, platform constraints, crisis-sensitive discourse, institutional communication, and the sociohistorical fields in which texts circulate. A translator working on legal updates, health messaging, diplomatic statements, or multilingual cultural programming must combine subject knowledge with discursive tact. AI can assist with lexical retrieval, but it cannot replace the embodied professional judgment that links domain knowledge to communicative responsibility (Olohan, 2016).

The purpose of this article is therefore threefold. First, it seeks to contextualize the rise of AI within contemporary media translation workflows. Second, it identifies the principal limits of algorithmic translation in environments where meaning depends on discourse, culture, and multimodal framing. Third, it proposes a reconceptualization of the specialized translator as an intercultural mediator whose competence must be redefined accordingly. The argument culminates in a pedagogical reflection on translator education, suggesting that future training should place greater emphasis on revision, ethical reasoning, discourse awareness, and technological literacy rather than on a static opposition between human and machine.

1. Problem Statement and Research Question

The problem addressed in this study arises from a structural contradiction within contemporary media production. Media institutions increasingly rely on artificial intelligence because multilingual content creation has become inseparable from the imperatives of immediacy, large-scale production, and cross-platform circulation. Yet, the more translation is accelerated and industrialized, the more visible become the limits of purely algorithmic solutions, especially when texts carry strong pragmatic, cultural, ideological, or affective weight. In this context, the central issue is no longer whether AI can generate target-language output, but whether such output is sufficient for communication in environments where interpretation has social, political, and symbolic consequences. Against this background, this article asks the following question: to what extent is the specialized translator being repositioned from handler of algorithmic output to intercultural mediator in AI-augmented media environments, and what consequences does this shift entail for professional competence and translator education?

A first dimension of the problem concerns the discourse of optimization that surrounds AI in professional settings. Artificial intelligence is often presented as a tool that guarantees faster turnaround, lower costs, and wider dissemination. These advantages are real and should not be dismissed. However, optimization is not a neutral category in media communication, because it presupposes implicit criteria of adequacy, relevance, acceptability, and tolerable loss. Once these criteria are examined more closely, it becomes evident that much of what matters in media translation cannot be reduced to speed or throughput. Headline framing, tonal calibration, symbolic references, crisis-sensitive wording, and audience trust all depend on qualitative decisions that remain irreducible to automated prediction alone.

A second dimension lies in the deceptive fluency of AI-generated output. In news and audiovisual translation, the most significant problems are not always visible at the surface of the text. They often concern implicature, irony, register, political connotation, intertextual resonance, or culturally marked references. Because machine-generated content frequently appears grammatically well formed, the human professional is no longer tasked merely with correcting obvious mistakes. Rather, the translator must identify latent risks, assess discursive consequences, and, when necessary, reconstruct communicative intentions that automation has flattened, displaced, or neutralized. The challenge is therefore interpretive before it is corrective.

A third dimension is epistemological. In AI-mediated workflows, what counts as an acceptable translation is increasingly shaped by platform metrics, productivity targets, and interface design. These factors do not merely affect the pace of work; they also influence how quality is defined, evaluated, and institutionalized. As a result, the specialized translator occupies a contested space in which linguistic expertise intersects with technological governance, editorial policy, and organizational constraint. To understand this transformation, it is not enough to oppose human creativity to machine efficiency in abstract terms. What must be examined instead is the redistribution of professional agency and the emergence of new forms of responsibility within hybrid human-machine environments.

A fourth dimension concerns professional visibility. The more institutions depend on automated systems for draft generation, the more human intervention risks being reframed as secondary polishing rather than primary meaning-making. Such a managerial reframing is profoundly misleading. In practice, the most consequential decisions often occur after automatic generation: when terminological ambiguity is resolved, when a culturally dangerous phrasing is neutralized, when a sensationalist formulation is moderated, or when a subtitle is rewritten so as to preserve both timing and tone. Human intervention, far from being residual, becomes decisive precisely where communicative risk is greatest. The specialized translator’s work thus tends to become less visible at the very moment when its interpretive and ethical value becomes more crucial.

The guiding hypothesis of this article is that the specialized translator is increasingly being repositioned from a model of linguistic transfer toward a model of intercultural, editorial, and ethical mediation. This hypothesis implies that the translator’s value now lies less in first-draft production than in informed judgment, less in raw output volume than in interpretive responsibility, and less in mere linguistic conversion than in the capacity to negotiate between institutional discourse, technological constraint, and audience intelligibility. From this perspective, the specialized translator should not be understood as a peripheral corrector of algorithmic text, but as a central agent in the management of meaning under conditions of automation.

2. Contextualizing AI in Media Translation

To understand the translator’s changing role, one must first situate AI within the broader evolution of media communication. Over the last two decades, media translation has moved from relatively delimited operations toward highly integrated and distributed workflows. The rise of online publishing, streaming services, mobile interfaces, and real-time social dissemination has created a communicative environment in which the same content may circulate through multiple channels, formats, and linguistic markets almost simultaneously. Translation in this setting is inseparable from localization, versioning, adaptation, metadata management, and editorial synchronization (Jiménez-Crespo, 2013; Pérez-González, 2014).

AI enters this environment not as an isolated tool but as part of a larger logic of platformization. Content must be searchable, segmentable, reusable, and compatible with automated pipelines. Machine translation, speech recognition, terminology extraction, subtitling aids, and drafting assistance are therefore increasingly linked to content management systems and publication dashboards. This integration changes the temporal structure of translation work. Instead of a linear sequence in which a source text is fully stabilized before transfer, translators may now intervene at multiple points in an iterative process involving suggestions, revisions, formatting constraints, and publication feedback.

Media translation is especially affected because it is not governed solely by terminological precision. It is also shaped by rhythm, genre, visual framing, audience expectation, and political sensitivity. A headline, a caption, a subtitle, a social-media card, and a live-coverage banner do not operate according to the same constraints. Each involves a different relation between brevity and explicitness, between textual content and surrounding paratext, and between institutional voice and public reception. AI tools can accelerate processing across these formats, but acceleration does not eliminate the need for genre-specific judgment.

Furthermore, media discourse is intrinsically relational. It addresses publics situated within historically and culturally differentiated spaces. A formulation that appears neutral in one context may be inflammatory, paternalistic, euphemistic, or simply unintelligible in another. This is why research on global news translation has long emphasized that media transfer is bound up with selection, framing, reframing, and narrative positioning rather than transparent equivalence alone (Bielsa & Bassnett, 2009). AI systems, by contrast, tend to privilege statistically dominant solutions derived from available data, which may reinforce standardization at the expense of contextual tact.

One should also note that AI modifies not only textual production but relations between professional actors. Translators increasingly work alongside editors, developers, localization managers, accessibility teams, and data-driven quality controllers. Their activity becomes collaborative in new ways: terminology is shared through databases, revisions are tracked through interfaces, and output may be evaluated through dashboards combining linguistic and performance indicators. This collaborative environment demands a broadened professional literacy. Translators must understand where their decisions fit in a chain that is simultaneously editorial, technical, and organizational.

The current appeal of AI in media settings thus stems from real pressures but also from a partial misunderstanding of translational labour. Institutions often perceive the visible product of translation while underestimating the invisible work of anticipation, documentation, revision, and intercultural risk management that makes publication possible. Once AI produces a seemingly usable draft, this invisible labour becomes even easier to overlook. Yet it is precisely this labour that determines whether content remains communicatively responsible when transferred across linguistic and cultural boundaries.

Finally, AI encourages a shift from document-based translation to flow-based translation. Translators are asked not only to process individual texts but to manage streams of content, recurring updates, iterative versions, and platform-specific derivatives. In such conditions, consistency, responsiveness, and terminological memory gain importance, but so do discernment and prioritization. The translator must decide what deserves close revision, what can be lightly processed, and what requires reformulation from the ground up. These decisions are themselves part of professional expertise, not merely logistical arrangements.

It follows that AI should be treated less as a substitute for translation than as a factor reorganizing the ecology of media mediation. It alters the location of effort, the sequence of decisions, the forms of accountability, and the criteria of expertise. In this ecology, the translator’s role becomes more editorial and more diagnostic. The central task is no longer merely to produce language, but to decide when generated language is insufficient, misleading, too generic, ideologically skewed, or pragmatically maladjusted.

3. The Limits of Algorithmic Translation

The enthusiasm surrounding AI in translation frequently rests on advances in fluency and apparent naturalness. Neural systems can generate target texts that are smoother, faster, and more idiomatic than earlier rule-based or phrase-based models. However, surface fluency should not be mistaken for full communicative adequacy. The limits of algorithmic translation become evident as soon as one examines discourse not as a sequence of isolated sentences but as a situated act embedded in genre, history, institutional voice, and audience expectation (Cronin, 2013; Kenny, 2022).

A first limitation concerns pragmatics. AI systems often struggle with illocutionary force, presupposition, irony, hedging, insinuation, and evaluative nuance. In many media texts, what matters is not merely what is said but how the statement positions speaker and audience. A governmental press release, a humanitarian alert, an investigative headline, and a satirical commentary may share vocabulary while differing radically in pragmatic orientation. Algorithmic output may preserve lexical content yet neutralize or misplace the intended force, thereby altering the communicative function of the text.

A second limitation involves cultural density. Specialized media translation often requires attention to references that are not explicitly glossed in the source text: historical memories, institutional habits, religious sensitivities, social taboos, gender norms, or regionally coded metaphors. These features are difficult to process because they do not reside in lexical units alone. They are distributed across discourse, genre conventions, and shared background knowledge. AI may reproduce dominant formulations, but it cannot reliably determine whether those formulations are culturally acceptable, strategically appropriate, or narratively resonant for a specific readership.

Third, algorithmic translation remains fragile when confronted with ideological framing. Media discourse is rarely neutral in its distribution of agency, legitimacy, or sympathy. Lexical choices can mitigate or intensify violence, foreground or background responsibility, normalize or challenge institutional narratives. Because AI systems learn from large corpora that reflect prior discursive distributions, they may reproduce patterns of bias or default framing without any internal capacity for critique. The problem is not simply that models may be biased; it is that bias can be operationalized under the appearance of neutral efficiency.

A fourth limitation concerns terminology in context. In specialized translation, lexical equivalence is often governed by discourse function, domain convention, and audience literacy. A technically correct term may be inappropriate in public-facing media if it overcomplicates, obscures, or overstates. Conversely, a simplified term may become misleading if it suppresses a critical distinction. AI systems frequently optimize for the most statistically probable rendering rather than for the most discursively appropriate one. The translator must therefore evaluate terminology not in isolation but within communicative strategy.

A fifth limitation concerns multimodality. In audiovisual and digital media, translation interacts with images, sound, layout, timing, typography, and user-interface constraints. A subtitle is not only a sentence in another language; it is a time-bound and visually embedded unit. A caption coexists with framing, cropping, and platform design. A push notification competes for attention within a restrictive character space. AI can support segmentation and drafting, yet it does not by itself guarantee coherence between verbal content and multimodal environment. Human judgment remains necessary to manage condensation, rhythm, emphasis, and semiotic alignment (Pérez-González, 2014).

A sixth limitation concerns false confidence. In professional settings, the danger of AI lies not only in error frequency but in error detectability. A visibly awkward sentence invites scrutiny; a fluent but semantically displaced sentence often passes unnoticed until its consequences emerge in reception. This is particularly problematic in high-velocity media workflows where review time is compressed. The translator’s role then includes a form of vigilance against deceptive adequacy: identifying what looks correct while being pragmatically, institutionally, or ethically wrong.

A seventh limitation concerns accountability. When a translation error produces reputational damage, misinformation, offense, or legal risk, the responsibility cannot be meaningfully assigned to the model alone. Institutions must ultimately rely on human professionals to validate, revise, or block problematic outputs. This means that automation does not remove responsibility; it redistributes it in ways that can make human labour both more essential and less visible. The translator is often the final guarantor of a text that has been partially generated elsewhere, sometimes under strong temporal and managerial pressure.

An eighth limitation concerns linguistic inequality. High-resource languages benefit disproportionately from data-rich training environments, whereas low-resource languages, regional varieties, and culturally hybrid registers often remain poorly served. In media contexts, where marginalized publics may already experience symbolic underrepresentation, this asymmetry is consequential. AI can therefore intensify existing inequalities by encouraging institutions to privilege communicative routes that are easiest to automate. Human mediation becomes crucial not only for quality control but for preserving linguistic diversity and resisting the homogenization of discourse.

Taken together, these limitations do not prove that AI is useless. Rather, they clarify the domain in which human expertise retains decisive value. AI can accelerate drafting, support consistency, and reduce some repetitive burdens. But whenever translation entails pragmatic judgment, cultural calibration, ethical vigilance, multimodal coordination, or responsibility for public meaning, algorithmic output remains insufficient on its own. The translator’s task thus shifts from direct transfer to critical supervision, interpretive arbitration, and communicative stewardship.

4. The Human Translator as Intercultural Mediator in AI-Augmented Media

If AI reveals the limits of a purely transfer-based conception of translation, it also makes visible a more demanding professional identity. The specialized translator in media environments increasingly functions as an intercultural mediator. This designation should not be understood in a vague or ornamental sense. It names a concrete set of operations through which the translator interprets discursive intention, identifies potential zones of misunderstanding, negotiates between institutional voice and target-culture intelligibility, and modulates expression in light of the social consequences of publication.

This mediating role can be observed at several levels. At the textual level, the translator evaluates lexical choices, syntactic emphasis, and register. At the discursive level, the translator considers framing, narrative orientation, and the implicit positioning of actors. At the institutional level, the translator must often align the target text with editorial standards, legal precautions, and brand or newsroom identity. At the audience level, the translator anticipates reception, including the possibility of offense, ambiguity, or mistrust. The result is a multilayered competence that cannot be reduced to bilingual equivalence.

In AI-augmented workflows, post-editing therefore becomes an insufficient label if it is understood merely as correcting machine output. What is required is a form of critical revision capable of recontextualizing the text. The translator must determine not only whether the proposed formulation is grammatically acceptable, but whether it is communicatively just, semantically proportionate, and culturally calibrated. This is why revision competence acquires strategic importance in machine-rich environments (Mossop, 2020). The decisive question is not whether the machine has produced language, but whether the generated discourse can legitimately circulate under the institution’s name.

Intercultural mediation also includes documentary work. Specialized translators frequently consult parallel corpora, institutional precedents, technical glossaries, and public discourse in the target culture before validating a formulation. This documentation phase becomes more, not less, important in AI-rich environments, because machine outputs can create the illusion that a problem has already been solved. In reality, the output often serves only as a provisional hypothesis requiring confirmation. The mediator’s expertise lies precisely in knowing when to trust a suggestion, when to test it against external evidence, and when to reject it entirely.

Another crucial dimension is interaction with other professionals. Translators increasingly explain decisions to editors, product teams, accessibility specialists, and managers who may not share the same linguistic or cultural sensitivities. Intercultural mediation therefore involves argumentation as well as drafting. The translator must be able to justify why a certain wording is risky, why a culturally marked term cannot be neutralized without loss, or why a literal rendering would undermine audience comprehension. Professional authority is exercised not only in the target text, but also in the capacity to make translational judgment intelligible within organizations.

Intercultural mediation also implies resisting false transparency. Because AI outputs often appear smooth, organizations may be tempted to naturalize them and to treat remaining human intervention as marginal polishing. Yet smoothness can conceal erasures. A generated sentence may flatten conflictual terminology, neutralize a marked voice, depoliticize an expression, or replace a culturally grounded image with a generic formulation. The mediator’s task is therefore partly diagnostic and partly critical. It involves identifying what has been lost, normalized, or ideologically displaced in the movement from source discourse to machine-generated target text.

This role is especially significant in multilingual public communication. Media translation does not simply move information across languages; it participates in the construction of publics. By choosing how actors are named, how events are framed, and how uncertainty is expressed, translation contributes to the symbolic organization of shared reality. When these operations are mediated by AI, the human translator becomes the site at which technological output is reinserted into social responsibility. In that sense, the translator is not external to technological systems but acts as the interpretive threshold through which automation must pass before public circulation.

The notion of mediation also helps clarify why the translator’s expertise cannot be captured by productivity metrics alone. A translation may be rapid yet inadequate, consistent yet politically tone-deaf, fluent yet ethically problematic. Conversely, the value of human intervention often lies in preventing an error that never becomes visible because it was intercepted before publication. Much of the translator’s work is therefore counterfactual: avoiding misalignment, pre-empting misreading, and safeguarding trust. This preventative and curatorial dimension is central to professional practice, even though it often remains invisible in managerial descriptions of workflow.

To conceptualize the specialized translator as an intercultural mediator is thus to affirm that translation in AI-saturated media is increasingly a practice of judgment. It requires linguistic competence, but also discursive analysis, technological awareness, institutional literacy, and ethical reflexivity. The translator must know how systems work, but also where they fail; must understand genre conventions, but also when those conventions need to be renegotiated for another audience; must revise language, but also defend meaning against the pressures of speed, scale, and standardization. This is not a diminished role. It is, on the contrary, a more complex and more consequential one.

5. Pedagogical Implications for Translator Education

If the translator’s role is being redefined, translator education cannot remain organized around older models that separate linguistic transfer from technological mediation. The challenge is not simply to add a software module to an otherwise unchanged curriculum. What is required is a broader pedagogical reorientation capable of treating AI as part of the environment in which translators will think, revise, negotiate, and justify their decisions. This means that training must move beyond tool familiarization toward a critical understanding of automated systems and their effects on professional responsibility.

A first implication concerns technological literacy. Students should learn how machine translation, speech technologies, large language models, and translation memories function at a conceptual level. The objective is not to turn translators into engineers, but to give them sufficient understanding to evaluate outputs, identify failure patterns, and resist uncritical dependence on interface authority. Without such literacy, automation is too easily perceived as either magical efficiency or existential threat. Both perceptions are pedagogically sterile.

A second implication concerns revision as a central competence rather than a secondary corrective exercise. In AI-augmented environments, the ability to diagnose inadequacy is at least as important as the ability to draft from scratch. Training should therefore include systematic practice in post-editing, comparative revision, error typology, and justification of choices. Students must learn to explain why an apparently fluent output remains unacceptable because of discourse, pragmatics, ideology, or audience positioning. This is entirely consistent with broader competence-based approaches that place reflective decision-making at the core of professional expertise (Pym, 2013; European Commission, 2022).

A third implication concerns discourse analysis and intercultural reasoning. Translators operating in media environments need to recognize framing effects, narrative positioning, mitigation strategies, and value-laden terminology. They must also be able to identify culturally sensitive elements that call for adaptation, explicitation, restraint, or deliberate retention. In this sense, translator education should cultivate not only bilingual agility but a strong capacity for reading texts as situated interventions. Courses in discourse analysis, media genres, and intercultural communication are therefore not ancillary; they are integral to professional formation.

A fourth implication concerns multimodality. Because so much media translation now unfolds across subtitles, captions, interfaces, podcasts, short-form video, and social media assets, students should be trained to work with time, space, image, and platform-specific formatting constraints. Exercises should expose them to the relation between verbal choice and audiovisual environment, between headline brevity and semantic precision, and between interface affordances and translational decision-making. Such training helps future professionals understand that translation is often a semiotic negotiation rather than a purely verbal transfer.

A fifth implication concerns pedagogy itself. Case-based teaching, simulated multilingual workflows, collaborative revision sessions, and annotated post-editing tasks are particularly well suited to the present moment. They allow students to confront realistic constraints while making their decision processes explicit. Instead of treating translation as the production of a single correct answer, such methods foreground uncertainty, justification, and professional reasoning. This is essential if training is to prepare students for environments in which they will routinely evaluate machine-generated alternatives rather than simply replace them with their own first drafts.

A sixth implication concerns ethics. AI-rich translation settings create new forms of invisibility, delegation, and responsibility. Students must therefore be equipped to reflect on authorship, accountability, bias, confidentiality, and linguistic inequality. Ethical training should not be limited to abstract principles but connected to realistic scenarios: crisis communication, sensitive political terminology, health messaging, and under-resourced language contexts. The point is to prepare translators to recognize when efficiency conflicts with responsibility and how to argue professionally for necessary human intervention.

A seventh implication concerns assessment. If institutions continue to evaluate students only on final product equivalence, they will miss the competences that now define professional value. Assessment should therefore include revision reports, commentary on rejected AI suggestions, workflow rationales, and evidence of documentation strategies. Such instruments make visible the intellectual labour of mediation and help legitimize forms of expertise that are otherwise hidden behind apparently seamless target texts.

Finally, translator education should encourage forms of reflective practice that make students aware of their own decision processes. Annotated revisions, process diaries, corpus-based comparisons, and team-based simulations can help them articulate how they evaluate AI suggestions and why they reject, modify, or retain them. This explicit reflexivity is crucial because future translators will increasingly need to justify their expertise within institutional environments that may undervalue what cannot be counted easily. To train translators for AI-augmented media is therefore to train them not only to work with technologies, but also to defend the epistemic value of human judgment within technological systems.

Conclusion

The rise of AI in media translation does not abolish the need for specialized translators. What it abolishes, or at least destabilizes, is a narrow understanding of translation as linear linguistic substitution. Once translation is integrated into accelerated, multimodal, and platformized workflows, the most valuable human contribution shifts toward interpretation, revision, calibration, and responsibility. The translator becomes less a producer of first-draft language in isolation and more a mediator who ensures that discourse remains communicatively, culturally, and ethically viable in circulation.

This article has shown that the limits of algorithmic translation are not marginal imperfections awaiting technical correction. They concern dimensions that are constitutive of media communication itself: pragmatics, framing, cultural density, multimodality, accountability, and linguistic justice. These dimensions explain why human intervention persists even where automation is widely adopted. They also explain why the translator’s role is becoming more complex rather than less relevant.

Reframing the specialized translator as an intercultural mediator allows us to move beyond sterile narratives of replacement. The key issue is not whether machines can produce text, but whether institutions can responsibly communicate across languages without the forms of situated judgment that human translators provide. In AI-augmented media environments, that judgment is exercised through revision, editorial negotiation, discursive sensitivity, and the capacity to anticipate the consequences of wording in public space.

The pedagogical implication is clear. Translator education must prepare professionals who are technologically literate without being technologically subordinate, critically reflective without being technophobic, and linguistically skilled without reducing translation to language alone. The future of specialized translation in media environments will depend on the recognition of this expanded competence. Far from being a residual figure in an automated chain, the translator remains the agent through whom multilingual communication becomes genuinely interpretable, accountable, and culturally inhabitable.

Future research may extend this discussion by comparing concrete post-editing practices across media sectors, by examining how translators negotiate authority within platformized organizations, and by documenting the particular challenges posed by low-resource languages in AI-driven publication environments. Such work would help move the debate from abstract predictions toward a more grounded sociology of translational mediation under technological pressure.