Is it AI or PE that worry translation professionals: results from a Human-Centered AI survey (2025)

Abstract: Translation technologies have historically been developed without substantial input from professionals (e.g. O’Brien 2012). Conversely, the emerging human-centered AI (HCAI) paradigm emphasizes the importance of including end-users in the “process of conceiving, designing, testing, deploying, and iterating” technologies (Vallor 2024: 17). Therefore, early research engagement on the attitudes, needs and opinions of professionals on AI implementation is essential because incorporating them at later stages “results in issues and missed opportunities, which may be expensive to recover from due to the cost, time, resources, and energy spent” (Winslow and Garibay 2004: 123). To this end, this article presents a qualitative analysis of professional translators’ attitudes towards AI in the future, centered around the role of MT and post-editing (PE). The discussion draws on data collected from open ended questions included in a larger survey on control and autonomy from a HCAI perspective, which were thematically coded and qualitatively examined. The thematic analysis indicates that predominant concerns regarding the future of the AI-driven translation industry still revolves around longstanding issues in PE and MT literature, such as PE, translation quality, communicating and educating LSP, clients, users, and the broader public, maintaining human control over the final product or creativity. This is explained to some extent to the relatively small rates of integration of AI technologies into translation workflows to date (e.g. ELIA 2024; Rivas Ginel et al 2024; GALA 2024; Jimenez-Crespo 2024), or the fact the professional report using AI primarily for tasks related to translation, but not necessarily to PE the output of LLMs or NMT (Rivas Ginel and Moorkens 2025).

## introduction the launch of chatgpt by the company openai in november of 2022 started a revolution that was intended to transform a large number of fields @xcite . large language models (llms) and different generative ai apps have been gradually implemented across professional fields, with translation and interpreting identified as an area of high exposure to negative impacts of ai @xcite . in this context, concerns regarding the impact of ai have led to the emergence of the multidisciplinary field of human-centered ai (hcai). this area of inquiry aims to position humans at the centered of technological developments @xcite , thereby ensuring that "their values and agency [are taken] into account" @xcite . in countering the prevalent hype in the ai industry, hcai represents "a paradigm shift, moving beyond the prevalent technology-centered approaches towards ai driven by human values" @xcite . a key issue addressed in this paper is that, even when ai and llms are supposed to revolutionize translation and interpreting practices, they are in fact not human-centered technologies @xcite . scholars have argued this because llms were developed without a clear focus on the needs, demands or preferences of existing end users. instead, they emerged because evolving architectures and processing capabilities allowed companies, such as openai, to successfully implement them (ibid). nevertheless, they originally came without guardrails or clearly defined professional use-cases unsupervised use beyond the industry hype. this lack of human centeredness for professional tasks means that over the last two years, a large body of research has been devoted to how, when or to what extent llms might be perceived as useful or and can be successfully integrated in professional tasks. in the language industry, gen-ai and llms have been integrated @xcite @xcite , often through trial and error and careful testing, in a wide range of tasks that include machine translation (mt), mt evaluation or automatic post editing (ape). both industry @xcite and scholarly publications (rivas @xcite include a wide range of tasks in addition to translation. for example, recent studies have shown that professionals primarily use llms for tasks such as generating inspiration, summarizing content, rephrasing texts, understanding technical expressions, or performing terminology-related tasks (rivas @xcite . nevertheless, translation is not reported as the most frequent use. in this context, this paper reports on a qualitative section of a wider survey (jiménez-crespo, 2024) on attitudes towards the future impact of ai in three key areas of human-centered ai approaches, control, autonomy, and automation @xcite . the need for this type of research is evident, as a key principle of hcai approaches emphasizes the active participation of end-users throughout "process of conceiving, designing, testing, deploying, and iterating" technologies @xcite . @xcite stress the importance of "involv[ing] potential users from the early stages of product and service development" because having an "inclusive r&d process is imperative" (2024: 3). they need to be incorporated in the early stages of ai development and deployment because incorporating them at later stages "results in issues and missed opportunities, which may be expensive to recover from due to the cost, time, resources, and energy spent" @xcite . as ai technologies continue to advance, the understanding of user opinions and attitudes are critical for their successful adoption into the translation workflows. such understanding helps to mitigate the risk of these technologies being perceived negatively, as imposed or restrictive by end-users @xcite . these negative perceptions often lead to challenges with technology adoption and reduced job satisfaction @xcite . the qualitative data analyzed for this paper focuses specifically on discourses by professionals surrounding machine translation post-editing (mtpe) on open ended questions related to future challenges posed by ai, as well as how automation might impact the techno sociological work conditions of translators in the usa. published quantitative results (jiménez-crespo, 2024) 1 from the same survey study showed high self-reported levels of "perceived control" and "autonomy" over translation technologies, and subjects reported medium levels of forced technology use. future perceived control in an ai era declined, but this perceived loss of control in the ai era was attributed to human agents in the process rather than ai apps or algorithms (big tech, developers, language service provicers (lsps), project managers, clients, etc.). ## methodology this mix methods study involved a selfadministered online qualtrics survey available to professional us-based translators. the study obtained ethical clearance by rutgers university ethical board and was piloted and revised. the survey was made available until june 15 th , 2024 and 50 participants completed the survey. participants were recruited online via e-mailings through all major professional associations in the us (e.g., ata language technology division, ata spanish division, north-eastern chapters of the american translators association) and social networks (e.g. linkedin). the only requirement to participate was to be a full-time translator in the usa with more than 2 years of experience. to encourage participation, a snowball sampling method was used @xcite . qualitative data in this paper were analyzed through thematic content analysis @xcite , utilizing a coding scheme that was developed inductively from emerging patterns in the data, then iterated, and finally used to categorize all responses. the bottom-up inductive analysis resulted on a coding scheme based on patterns in existing responses across the dataset. this initial set was used then by an additional researcher, and the coders then met to discuss any differences and to refine the scheme. using this approach, the proportion of responses within each group that corresponded to a specific code were calculated, representing a theme identified in the dataset. the survey had a final section with openended questions related to the future of ai-driven translation technology integrations in the hcai era. this section included five questions that provided the data analyzed in the present paper: question 23a: human-centered ai and the future human-centered ai involves a high degree of automation with humans firmly in control of the overall process. imagine that in the near future you will work in a translation platform or translation management system powered by ai integrations. […] do you think you will have control over the integrations of ai in the translation process? please explain question 25: which part or subcomponents of the translation process do you think you might lose control over as ai becomes increasingly integrated into the workflow? question 26: if you had to provide input to design an ai technology tool to augment your capacities to translate better, more efficiently, or faster, how would you describe it? question 27: human-centered ai involves a high degree of autonomy of the human agent(s). if you would develop ai applications for translation, what would "autonomy" mean for you? question 28: in your opinion, what are the main challenges translators might face in the age of automation and ai? all questions were optional, and participants could skip or not answer specific questions to avoid "survey fatigue" (davis, 2019). the following responses were recorded for each question: @xmath0, @xmath1, @xmath2, @xmath3, @xmath4. the total responses recorded for openended questions dealing with an ai driven future were 182. the focus of the present analysis is on those themes and subthemes related to mtpe and mt, as well as conditions and issues related to these practices. ## limitations of the study the study had certain limitations. first and foremost, the size of the sample. as previously mentioned, the call for participation was posted on the main professional forums in the usa and several chapters of the american translators association. it is possible that both the extensive nature of the survey and the theoretical approach that focused on certain aspects related to hcai, control and autonomy might have discouraged potential participants or prevented them from completing the survey. in addition, no direct compensation was offered for participation. second, it is possible the "survey fatigue" (davis, 2019) might have influenced response rates, given the large numbers of national and international ai survey studies. nevertheless, it can be argued that the study is representative of the targeted population to the extent that some results overlap with similar much larger surveys in europe. for example, the elis (2024) and the rivas @xcite survey identified a 37-40% use of ai and llms by professionals in mid 2024, the same as the present study (jiménez-crespo 2024). while the relatively low response rate might be due to a methodological issue in the instrument design, the substantial data compiled provides a clear snapshot of the current attitudes towards ai. in terms of replicability, the complete survey is already accessible in an open science, freely accessible journal for replicability in other regions or settings (jiménez-crespo 2024). ## conclusions this qualitative study explored the attitudes towards ai, particularly focusing on hcai issues, such as control and autonomy, in the context of mtpe and the mt capabilities of recent ai driven llm models. the survey was responded by 50 usbased professionals and the present study focused on five open ended questions about the future impact of ai on their profession, future job conditions, autonomy or how they envision an "augmented" future. overall, the results show that the main future challenges and attitudes towards ai technologies in the ai era primarily center on pe, control over the initial transfer process from the source text, and translation quality. this is followed by themes, such as communicating and educating lsp, clients, users, and society at large, human control over the final product and the ability to turn on and off technologies or decide when and how to use them. other less frequent themes emerge as key areas of concern depending on the question posed to respondents, such as creativity, and the attitudes toward adaptive or interactive technologies. when asked about the main challenges posed to translators, issues such as human replacement or rates also appear as key themes. in summary, the thematic analysis of the dataset reveals that those current concerns regarding the ai-driven future still revolve around established issues in pe and mt literature. notably, several themes and subthemes that frequently appear in both ts and hcai literature were less present in these responses towards the future of the profession in an hcai era, such as data biases, ethics, or usability. current attitudes toward an ai-augmented future remain predominantly characterized by established concerns, such as resistance to pe, questions about whether relinquishing the initial draft translation (the transfer stage) fundamentally alters the essence of "translation," and related implications for quality, compensation rates, and creative expression. in terms of what professionals' expressed demands, the main themes in the data revolve around developing adaptive or interactive mt and llm technologies and the full ability to control the final product without impositions such as locked segments, terminology, or the ability to control or override any ai implementations. this study addresses calls for further research into translators' attitudes towards translation technology in mt and ai era @xcite , and demonstrates the need of a humancentered approach to foster translators' well-being, satisfaction and rates of adoption of technologies.