AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT

Yilmaz, Erdem; Naumovska, Ivana; Aggarwal, Vikas A.

doi:10.2139/ssrn.4400516

Cited by 6 publications

(3 citation statements)

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“…As Felten et al (2021) argue, AI is important because it may impact various fields of study. First, much AI literature emphasizes whether technology will assist or displace workers, or more poignantly, which workers and skills will be augmented, enhanced, or replaced (Acemoglu & Restrepo, 2019;Felten, Raj & Seamans, 2021;Goldfarb et al, 2019;Yilmaz, Naumovska, & Aggarwal, 2022). This discussion generates implications for the changing value of human capital, particularly as individuals shift their skills (Horton & Tambe, 2019) or exploit changes in the value of human capital to move between employers (Jain & Huang, 2022;Miric & Ozalp, 2022).…”

Section: Implications Of Studying Artificial Intelligence For Theorie...mentioning

confidence: 99%

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Miric

Jia

Huang

2022

Strategic Management Journal

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Research Summary Researchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development. Managerial Summary Text‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research.

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Section: Implications Of Studying Artificial Intelligence For Theorie...mentioning

confidence: 99%

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Miric

Jia

Huang

2022

Strategic Management Journal

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“…The book additionally delves into the pragmatic applications, ethical implications, and potential future developments of ChatGPT, rendering it an allencompassing resource that facilitates comprehension and utilization of this technological innovation. In their study, Yilmaz et al (2023) conduct an exhaustive examination and furnish persuasive empirical data in support of the concept that experienced knowledge workers can substitute for human labor in a variety of positions. This discussion explores the implications of our findings with respect to the research on competitive advantage, technology adoption, and the micro-level underpinnings of strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing translation accuracy in Belt and Road Malaysia children's literature: Malay and Chinese native speakers vs ChatGPT

Lau,

Yong,

Chia

et al. 2024

Forum Linguist. Stud.

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The study investigates the translation processes of human and artificial intelligence translators in comparison. Human translators consist of a Chinese native speaker and belt and road translators. Different versions of artificial intelligence translators comprise ChatGPT 3.5 and ChatGPT 4.0. The research methodology employed is a keyword detection technique. One human translator and one translator powered by artificial intelligence achieved the highest scores in keyword detection, according to the results. Human translators continue to be indispensable in the field of translation, particularly in the translation of literary works. However, the research group is optimistic that artificial intelligence will soon be able to resolve this issue.

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“…Transcreation thus includes interventions such as addition, omission, updating, functional adaptation, re-formatting and change of media (e.g., Taibi & Ozolins, 2016;Taibi, 2018;Lesch, 2018), all of which can be conceptualised as translation solutions (Pym, 2016) but fall short of complete re-narration by a new first person. In terms of process, transcreation can embrace "interactive tasks that entail adaptation of meaning to cultural and emotional elements and influencing people", tasks that have been found to be highly automation-resistant (Yilmaz et al, 2023). In theory, failure to use transcreation can result in messaging that is potentially culturally inappropriate and thus ineffective.…”

Section: Introductionmentioning

confidence: 99%

On the transcreation, format and actionability of healthcare translations

Sengupta,

Pym,

Hao

et al. 2024

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In public-health crises, members of multilingual communities must be able to access, understand, trust and act upon behaviour-change messaging. The role of translators is therefore critical, not only for the relaying of information but also in the transcreation of texts, understood as adaptation to suit the characteristics of an intended audience. Failure to use transcreation may produce messaging that is culturally inappropriate and thus ineffective. This study analyses healthcare resources created by governments in Australia with a view to identifying formatting and other visual features that would benefit from transcreation. A mixed-method approach combined numerical evaluation of four documents using the Patient Education Materials Assessment Tool (PEMAT) and a bottom-up thematic analysis of the way the same texts were discussed by 58 members of a broad range of ethnocultural and linguistic groups in Victoria, Australia. The findings point to a need to go beyond the linguistic aspects of the translation and take into account the discourse organisation, layout, images and cultural appropriateness of health messaging. The implications of applying the PEMAT criteria are not only that start texts will become more accessible and better able to facilitate understanding-based trust relations, but also that translators are well placed to participate in the transcreations that may be required in the various target languages.

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AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT

Cited by 6 publications

References 0 publications

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Comparing translation accuracy in Belt and Road Malaysia children's literature: Malay and Chinese native speakers vs ChatGPT

On the transcreation, format and actionability of healthcare translations

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