{"id":9315,"date":"2025-11-20T13:35:45","date_gmt":"2025-11-20T18:35:45","guid":{"rendered":"https:\/\/cntiwpedev.wpenginepowered.com\/cnti-news\/\/"},"modified":"2026-02-18T17:59:52","modified_gmt":"2026-02-18T22:59:52","slug":"ai-transcription-and-translation-in-journalism","status":"publish","type":"post","link":"https:\/\/cnti.org\/reports\/ai-transcription-and-translation-in-journalism\/","title":{"rendered":"AI Transcription and Translation in Journalism"},"content":{"rendered":"\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-Introduction\">Introduction<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">CNTI&#8217;s AI and Journalism Research Working Group looked at 55 research studies and other articles from computer science, social science and linguistics disciplines to better understand how AI is shaping transcription and translation and what these developments mean for journalism. These studies include data representing a range of geographic contexts and languages.<\/p>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"padding-top:var(--wp--preset--spacing--40);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\n<details class=\"wp-block-mbm-innerblock-accordion is-layout-flow wp-block-mbm-innerblock-accordion-is-layout-flow\">\n<summary class=\"wp-block-mbm-innerblock-accordion-title\">\n<h4 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-echo-font-size\" style=\"margin-right:var(--wp--preset--spacing--50);margin-bottom:0px;line-height:1.25\">About<\/h4>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--open\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Open accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M17 10.5V15H21.5H22.5V17H21.5H17V21.5V22.5H15V21.5V17H10.5H9.5V15H10.5H15V10.5V9.5H17V10.5Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--closed\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Close accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M22.5 17H21.5H10.5H9.5V15H10.5H21.5H22.5V17Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n<\/summary>\n\n\n\n<div class=\"wp-block-mbm-innerblock-accordion-body\">\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">This is the second in a series of reports from the AI and Journalism Research Working Group convened by the Center for News, Technology &amp; Innovation (CNTI). The working group currently consists of 18 cross-industry members from around the world, bringing research, journalism and technology expertise to the discussions.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">The goal of the working group is to offer succinct summaries of global research in specific topics at the intersection of journalism and AI. Each quarter, the working group will synthesize the state of research across two to three topics for journalism practitioners, researchers and industry leaders around the world, focusing on actionable recommendations for journalism \u2014 not other fields that are concerned with AI.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">In each report, we lay out the general findings of the research to date, suggested considerations and\/or actions for practitioners and areas where more or new research is needed. This report was prepared by the research and professional staff of CNTI in partnership with several external contributors who collectively authored this briefing. If you have ideas or research findings that are important for CNTI and the working group to include, please email them to info@cnti.org.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>What do we mean by \u201cAI\u201d?<\/strong><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">This report uses the <a href=\"https:\/\/oecd.ai\/en\/ai-principles\" target=\"_blank\" rel=\"noreferrer noopener\">OECD definition<\/a>: \u201cAn AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Wherever possible, we try to use specific terms rather than \u201cAI\u201d to avoid conflation or confusion. Journalism has been adopting forms of automation for more than 50 years,<sup data-fn=\"29d91891-463f-43ac-80ab-b581d53f073a\" class=\"fn\"><a href=\"#29d91891-463f-43ac-80ab-b581d53f073a\" id=\"29d91891-463f-43ac-80ab-b581d53f073a-link\">1<\/a><\/sup> but widespread use of the term \u201cAI\u201d is more recent \u2014 and may include both newer technologies and those that have been in use for quite some time.<\/p>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:16px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"padding-top:var(--wp--preset--spacing--40);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\n<details class=\"wp-block-mbm-innerblock-accordion is-layout-flow wp-block-mbm-innerblock-accordion-is-layout-flow\">\n<summary class=\"wp-block-mbm-innerblock-accordion-title\">\n<h4 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-echo-font-size\" style=\"margin-right:var(--wp--preset--spacing--50);margin-bottom:0px;line-height:1.25\">Findings<\/h4>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--open\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Open accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M17 10.5V15H21.5H22.5V17H21.5H17V21.5V22.5H15V21.5V17H10.5H9.5V15H10.5H15V10.5V9.5H17V10.5Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--closed\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Close accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M22.5 17H21.5H10.5H9.5V15H10.5H21.5H22.5V17Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n<\/summary>\n\n\n\n<div class=\"wp-block-mbm-innerblock-accordion-body\">\n<p class=\"has-aktiv-grotesk-font-family wp-block-paragraph\">The research suggests:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size\">Journalists are actively using AI tools for both transcription and translation, but they experience varying levels of difficulty accessing the tools and varying accuracy of the outputs, due to geography, resources and other factors.<sup data-fn=\"80797874-c08d-485c-8360-3ebd232eeb08\" class=\"fn\"><a href=\"#80797874-c08d-485c-8360-3ebd232eeb08\" id=\"80797874-c08d-485c-8360-3ebd232eeb08-link\">2<\/a><\/sup><\/li>\n\n\n\n<li class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size\">AI transcription and translation systems can save time, compared with a fully manual process. Still, human review of AI transcription and translation is critical for ensuring accuracy and identifying potential errors, missing information and language biases.<sup data-fn=\"199eccc4-97cd-42e4-87d7-3da827d6c1e8\" class=\"fn\"><a href=\"#199eccc4-97cd-42e4-87d7-3da827d6c1e8\" id=\"199eccc4-97cd-42e4-87d7-3da827d6c1e8-link\">3<\/a><\/sup>&nbsp;The most promising workflows make it easy for humans to review,<sup data-fn=\"a78f92c3-ce43-4917-a1df-dc50ad787328\" class=\"fn\"><a href=\"#a78f92c3-ce43-4917-a1df-dc50ad787328\" id=\"a78f92c3-ce43-4917-a1df-dc50ad787328-link\">4<\/a><\/sup>&nbsp;and research suggests human review remains necessary for several reasons, especially in public-facing contexts.\n<ul class=\"wp-block-list\">\n<li>AI tools for transcription and translation are rapidly improving, but significant gaps remain for \u201clow-resource\u201d languages (i.e., languages with relatively little textual data online that can be used to train AI models).<sup data-fn=\"b1fcb2fd-e7bd-4632-957a-2b792987c69e\" class=\"fn\"><a href=\"#b1fcb2fd-e7bd-4632-957a-2b792987c69e\" id=\"b1fcb2fd-e7bd-4632-957a-2b792987c69e-link\">5<\/a><\/sup>&nbsp;Most of the languages spoken today are considered \u201clow-resource\u201d because there is not sufficient content available online, including languages spoken by tens or even hundreds of millions of people. Even among English speakers, only a limited variety of accents and dialects are transcribed at least mostly correctly by AI-mediated communication software.<sup data-fn=\"833de0f1-bc75-4c12-afea-78e42d298801\" class=\"fn\"><a href=\"#833de0f1-bc75-4c12-afea-78e42d298801\" id=\"833de0f1-bc75-4c12-afea-78e42d298801-link\">6<\/a><\/sup><\/li>\n\n\n\n<li>Training data can produce inherent biases in AI translation and transcription tools,<sup data-fn=\"621b7148-c218-4ef2-9fed-8e348a66c6d8\" class=\"fn\"><a href=\"#621b7148-c218-4ef2-9fed-8e348a66c6d8\" id=\"621b7148-c218-4ef2-9fed-8e348a66c6d8-link\">7<\/a><\/sup>&nbsp;which can lead to inaccurate outputs for journalistic content.<\/li>\n\n\n\n<li>AI tools are \u201cepistemologically indifferent\u201d<sup data-fn=\"6a102a13-4fac-4a52-be84-c14155d92e05\" class=\"fn\"><a href=\"#6a102a13-4fac-4a52-be84-c14155d92e05\" id=\"6a102a13-4fac-4a52-be84-c14155d92e05-link\">8<\/a><\/sup>&nbsp;to truth, meaning they are stochastic models that generate words based on probabilities and do not have a way to determine truth. This is one reason many existing tools vary in the quality of their outputs for transcription and translation.<sup data-fn=\"01d27cb9-1f34-4a73-b3bf-fac1f54cf49e\" class=\"fn\"><a href=\"#01d27cb9-1f34-4a73-b3bf-fac1f54cf49e\" id=\"01d27cb9-1f34-4a73-b3bf-fac1f54cf49e-link\">9<\/a><\/sup><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Artificial intelligence (AI) systems are increasingly being used for transcription \u2014 the process of converting audio to written form \u2014 and translation \u2014 the process of converting content in a source language to a target language. Journalist-facing transcription tools like Otter and Trint launched nearly ten years ago,<sup data-fn=\"21e0a365-4f3e-4b3a-a245-e6c249bba61d\" class=\"fn\"><a href=\"#21e0a365-4f3e-4b3a-a245-e6c249bba61d\" id=\"21e0a365-4f3e-4b3a-a245-e6c249bba61d-link\">10<\/a><\/sup> and automated translation has been available to everyday users \u2014 at least for a limited set of languages \u2014 since the release of Google Translate in 2006.<sup data-fn=\"336865ee-1683-451b-a785-1ac7bb9134c7\" class=\"fn\"><a href=\"#336865ee-1683-451b-a785-1ac7bb9134c7\" id=\"336865ee-1683-451b-a785-1ac7bb9134c7-link\">11<\/a><\/sup> Advances in AI systems mean that Google Translate and similar programs continue to improve rapidly.<sup data-fn=\"ef6fd1d3-d1cf-47be-8869-b52141ec2696\" class=\"fn\"><a href=\"#ef6fd1d3-d1cf-47be-8869-b52141ec2696\" id=\"ef6fd1d3-d1cf-47be-8869-b52141ec2696-link\">12<\/a><\/sup> However, journalists around the world do not uniformly experience the benefits of AI technologies in assisting with transcription and\/or translation. Inconsistent access to technology and varying availability of high-quality and verified training data remain major challenges.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Overall, AI models are improving. However, transcription tools remain most accurate for a relatively narrow range of standard American English dialects and accents,<sup data-fn=\"65c32dbe-12aa-4017-bd18-ded2abdf5db4\" class=\"fn\"><a href=\"#65c32dbe-12aa-4017-bd18-ded2abdf5db4\" id=\"65c32dbe-12aa-4017-bd18-ded2abdf5db4-link\">13<\/a><\/sup> and translation tools remain most accurate for only a few language pairs.<sup data-fn=\"fc15492a-4515-4f56-8403-af9083c04d75\" class=\"fn\"><a href=\"#fc15492a-4515-4f56-8403-af9083c04d75\" id=\"fc15492a-4515-4f56-8403-af9083c04d75-link\">14<\/a><\/sup> There are significant gaps in performance for \u201clow-resource\u201d languages (i.e., languages with relatively little textual data online that can be used to train AI models) as well as concerns about accuracy in those languages. It should be noted that \u201clow-resource\u201d languages include a number of languages spoken by hundreds of millions of people. In fact, English is the language of about 50% of online content; the largest proportion of online content that any other language represents is 6%.<sup data-fn=\"d1388023-9e20-4bf9-9ada-a222e545ee02\" class=\"fn\"><a href=\"#d1388023-9e20-4bf9-9ada-a222e545ee02\" id=\"d1388023-9e20-4bf9-9ada-a222e545ee02-link\">15<\/a><\/sup> Thousands of languages make only a minuscule imprint on the internet.<sup data-fn=\"7e516919-7202-43c7-b1e4-4230e84d8dac\" class=\"fn\"><a href=\"#7e516919-7202-43c7-b1e4-4230e84d8dac\" id=\"7e516919-7202-43c7-b1e4-4230e84d8dac-link\">16<\/a><\/sup> Some of the ongoing efforts to close the divide between \u201clow resource\u201d and \u201chigh resource\u201d languages focus on creating and improving training data. For example, Nigerian start-up Goloka is working with Meta to collect data in five Nigerian languages<sup data-fn=\"54d5d914-660f-40c0-ba61-52ee5ee74fec\" class=\"fn\"><a href=\"#54d5d914-660f-40c0-ba61-52ee5ee74fec\" id=\"54d5d914-660f-40c0-ba61-52ee5ee74fec-link\">17<\/a><\/sup> as part of a larger <a href=\"https:\/\/about.fb.com\/news\/2025\/02\/announcing-language-technology-partner-program\/\" target=\"_blank\" rel=\"noreferrer noopener\">Meta-UNESCO partnership<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI translation and transcription are rapidly developing areas of research. Nearly all the research articles cited in this briefing are from the last five years, and machine translation and transcription are steadily improving. The working group expects to see continued progress in building AI tools for these uses, especially for languages that have not received as much attention. However, there is also evidence that unverified AI translations are creating misinformation on sites like Wikipedia, and those low-quality translations are being used to train the next generation AI translation tools, leading to worse performance.<sup data-fn=\"23ae2852-56d5-48b3-bb99-25d1675f7754\" class=\"fn\"><a href=\"#23ae2852-56d5-48b3-bb99-25d1675f7754\" id=\"23ae2852-56d5-48b3-bb99-25d1675f7754-link\">18<\/a><\/sup><\/p>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-journalism-use-cases\">Journalism Use Cases<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Journalists regularly use AI tools for transcription and translation to assist in the production of news content, generally with human oversight over the process.<sup data-fn=\"6734b9e8-3840-4537-b1e2-6ff14d8284a1\" class=\"fn\"><a href=\"#6734b9e8-3840-4537-b1e2-6ff14d8284a1\" id=\"6734b9e8-3840-4537-b1e2-6ff14d8284a1-link\">19<\/a><\/sup> Journalists also use transcription tools, such as the Houston Chronicle\u2019s&nbsp;<a href=\"https:\/\/www.houstonchronicle.com\/projects\/meeting-monitor\/\" target=\"_blank\" rel=\"noreferrer noopener\">Meeting Monitor<\/a>, to share summaries of government meetings<sup data-fn=\"6f4c4029-6d87-467d-9edc-9147e53fd199\" class=\"fn\"><a href=\"#6f4c4029-6d87-467d-9edc-9147e53fd199\" id=\"6f4c4029-6d87-467d-9edc-9147e53fd199-link\">20<\/a><\/sup>&nbsp;with the public. Large-scale automated transcription and translation tools, including&nbsp;<a href=\"https:\/\/www.ebu.ch\/news\/2021\/06\/providing-a-european-perspectivepublic-service-media-allied-to-offer-an-innovative-news-sharing-model-across-the-continent\" target=\"_blank\" rel=\"noreferrer noopener\">A European Perspective<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/www.dubawa.ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">Dubawa<\/a>, are assisting journalists to report on topics that would otherwise be difficult and time consuming to monitor. A European Perspective consists of 10 broadcasters across nine European countries that exchange content using AI transcription and translation.<sup data-fn=\"cd18678a-1784-4761-ba39-7988ed05ecd2\" class=\"fn\"><a href=\"#cd18678a-1784-4761-ba39-7988ed05ecd2\" id=\"cd18678a-1784-4761-ba39-7988ed05ecd2-link\">21<\/a><\/sup>&nbsp;The automatic transcription of audio-visual content and translation between languages encourages greater coverage of European news topics while also using editorial oversight to correct cultural or linguistic details in outputs. Dubawa, an AI fact-checking system in Ghana and Nigeria, was specifically trained using local dialects and accents. The tool transcribes radio broadcasts and checks for mis- and disinformation in several local languages.<sup data-fn=\"33f2991b-81b7-4b31-82a9-ce590959530c\" class=\"fn\"><a href=\"#33f2991b-81b7-4b31-82a9-ce590959530c\" id=\"33f2991b-81b7-4b31-82a9-ce590959530c-link\">22<\/a><\/sup>&nbsp;In a similar vein, Paraguayan news outlet&nbsp;<em>El Surti&nbsp;<\/em>is building a community-based Guaran\u00ed language dataset and AI tools.<sup data-fn=\"9ed420a3-b60c-47c3-99aa-f7adbdda0ffd\" class=\"fn\"><a href=\"#9ed420a3-b60c-47c3-99aa-f7adbdda0ffd\" id=\"9ed420a3-b60c-47c3-99aa-f7adbdda0ffd-link\">23<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The degree to which AI systems have been implemented for transcription and translation depends on newsroom resources and languages in use. Through a series of semi-structured interviews in South Africa, one study finds that AI tools are mostly being integrated in larger newsrooms and used to aid in public-facing translation, particularly at public media outlets with mandates to publish in multiple official languages.<sup data-fn=\"8d065a39-271e-4932-9b7e-e47ff50491f0\" class=\"fn\"><a href=\"#8d065a39-271e-4932-9b7e-e47ff50491f0\" id=\"8d065a39-271e-4932-9b7e-e47ff50491f0-link\">24<\/a><\/sup> Still, journalists \u2014 especially those in the Global South \u2014 are concerned about the utility of AI tools for transcription and translation, given reported challenges with accents and lower accuracy for local languages.<sup data-fn=\"52871edc-dcac-49d9-aadd-ebd5c02aa7a1\" class=\"fn\"><a href=\"#52871edc-dcac-49d9-aadd-ebd5c02aa7a1\" id=\"52871edc-dcac-49d9-aadd-ebd5c02aa7a1-link\">25<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Newsrooms find that using AI tools for translation saves time but can result in inaccuracies, necessitating human review, especially in audience-facing content. One study found that AI translations of international news in Tanzania were mostly accurate, but about 13% of the sentences included mistranslations, minor ambiguities or inaccuracies that missed cultural details, such as incorrectly translating the English phrase \u201cstreet food\u201d word-for-word as \u201cfood of the road\u201d in Kiswahili, rather than using an idiomatic phrase.<sup data-fn=\"2642a422-56a4-4dec-be29-cf8479fc2761\" class=\"fn\"><a href=\"#2642a422-56a4-4dec-be29-cf8479fc2761\" id=\"2642a422-56a4-4dec-be29-cf8479fc2761-link\">26<\/a><\/sup>&nbsp;Similarly, a 2023 study benchmarking AI transcription and translation tools for journalists also found that they can save considerable time even with human review, but translations into English perform better than other languages.<sup data-fn=\"11aff7b0-73bf-4624-90c2-b33a147fa290\" class=\"fn\"><a href=\"#11aff7b0-73bf-4624-90c2-b33a147fa290\" id=\"11aff7b0-73bf-4624-90c2-b33a147fa290-link\">27<\/a><\/sup>&nbsp;In general, hybrid translation, in which machine translations are reviewed by human experts, is a promising approach.<sup data-fn=\"753cfebc-0969-4ed7-9c34-bda719695d92\" class=\"fn\"><a href=\"#753cfebc-0969-4ed7-9c34-bda719695d92\" id=\"753cfebc-0969-4ed7-9c34-bda719695d92-link\">28<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Translation and transcription tools can also be used to personalize news content for different audiences. For example, publishers can add widgets to their websites or apps that would allow audiences to access an automatically generated transcript of video or audio content, or to access all content in the language of their choice. Publishers are increasingly interested in providing these features.<sup data-fn=\"5e701cd0-1e7f-49e2-b074-5c77d07f1459\" class=\"fn\"><a href=\"#5e701cd0-1e7f-49e2-b074-5c77d07f1459\" id=\"5e701cd0-1e7f-49e2-b074-5c77d07f1459-link\">29<\/a><\/sup>&nbsp;However, publisher interest is currently far outpacing audience interest: according to the 2025 Reuters Digital News Report, 65% of publishers were actively exploring AI translation content but only 24% of audiences said they were interested in using AI translation.<sup data-fn=\"3509cb94-90b7-4508-9d96-35bcfb49c746\" class=\"fn\"><a href=\"#3509cb94-90b7-4508-9d96-35bcfb49c746\" id=\"3509cb94-90b7-4508-9d96-35bcfb49c746-link\">30<\/a><\/sup>&nbsp;Similarly, 75% of publishers were exploring making audio available in text format (and vice versa), but only 15% of audiences expressed an interest. The reasons for these gaps are beyond the scope of this report, but researchers have suggested they may be linked to a lack of awareness of what the features might look like, or to a broader audience distrust of AI in journalism.<sup data-fn=\"b920bdde-ccc9-4518-9d5a-bc533114acf1\" class=\"fn\"><a href=\"#b920bdde-ccc9-4518-9d5a-bc533114acf1\" id=\"b920bdde-ccc9-4518-9d5a-bc533114acf1-link\">31<\/a><\/sup>&nbsp;However, audiences are more comfortable with AI translation than with many other newsroom uses of AI.<sup data-fn=\"6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0\" class=\"fn\"><a href=\"#6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0\" id=\"6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0-link\">32<\/a><\/sup><\/p>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-what-level-ofaccuracy\">What Level of Accuracy is Good Enough?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">One question that has not been addressed by research, is what level of accuracy is good enough? For example, is it appropriate to use AI tools if translation and transcription reach a certain level of accuracy? Even a tool with 95% accuracy may still miss crucial cultural and language nuances. Thus, human review and revision are necessary to ensure accuracy and appropriateness for most public-facing content. Future research examining the value of different strategies for improving accuracy and appropriateness, such as diversifying sources of training data, metadata, language-specific models or algorithms, can help determine how to address accuracy standards. However, research can only inform what is, at its core, a value judgment.<\/p>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-technical-evaluations\">Technical Evaluations of AI Transcription and Translation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Models for these types of tasks are rapidly improving,<sup data-fn=\"e563f6f6-a7a5-458d-8c24-921d72436b69\" class=\"fn\"><a href=\"#e563f6f6-a7a5-458d-8c24-921d72436b69\" id=\"e563f6f6-a7a5-458d-8c24-921d72436b69-link\">33<\/a><\/sup> and translations between certain language pairs \u2014 like Spanish and English \u2014 generally perform well.<sup data-fn=\"00442057-afbf-4350-b456-6d338f1f0403\" class=\"fn\"><a href=\"#00442057-afbf-4350-b456-6d338f1f0403\" id=\"00442057-afbf-4350-b456-6d338f1f0403-link\">34<\/a><\/sup>&nbsp;Researchers find that while AI tools cannot currently handle cultural details and ambiguity as well as human experts can, they are promising in a number of other contexts, such as (1) translations of medical terms from English to German<sup data-fn=\"36cae93d-2512-48ae-89ac-9a10f7c3c3e3\" class=\"fn\"><a href=\"#36cae93d-2512-48ae-89ac-9a10f7c3c3e3\" id=\"36cae93d-2512-48ae-89ac-9a10f7c3c3e3-link\">35<\/a><\/sup>&nbsp;and (2) translations of legal documents across Arabic and English.<sup data-fn=\"0bd57973-aa4a-4949-a865-c2ca4f3672c0\" class=\"fn\"><a href=\"#0bd57973-aa4a-4949-a865-c2ca4f3672c0\" id=\"0bd57973-aa4a-4949-a865-c2ca4f3672c0-link\">36<\/a><\/sup>&nbsp;Researchers also find that AI translations from Indonesian to English often rival those of students in translation educational programs when it comes to implementing techniques like paraphrasing and structural transposition.<sup data-fn=\"a78df0d2-5c1e-461b-90cd-390c07838ad1\" class=\"fn\"><a href=\"#a78df0d2-5c1e-461b-90cd-390c07838ad1\" id=\"a78df0d2-5c1e-461b-90cd-390c07838ad1-link\">37<\/a><\/sup>&nbsp;Meanwhile, AI transcription is being used to increase coverage of government meetings.<sup data-fn=\"c7d859e3-e24d-466d-95cc-d6c3e5604228\" class=\"fn\"><a href=\"#c7d859e3-e24d-466d-95cc-d6c3e5604228\" id=\"c7d859e3-e24d-466d-95cc-d6c3e5604228-link\">38<\/a><\/sup>&nbsp;Summarizing these meetings may be a particularly fruitful use of AI transcription because participation follows a consistent structure and because figurative language and wordplay are rare. Progress in this field continues as research and development of novel techniques to build training datasets advances and as the development of specific translation and transcription models receive more attention.<sup data-fn=\"3bb91335-2924-4562-a784-9ac17a6dc44e\" class=\"fn\"><a href=\"#3bb91335-2924-4562-a784-9ac17a6dc44e\" id=\"3bb91335-2924-4562-a784-9ac17a6dc44e-link\">39<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While AI transcription and translation technologies are improving, recent research also highlights limitations and shortcomings of these AI tools<sup data-fn=\"2ff7a095-cf02-485f-bbad-c78473cb1dc3\" class=\"fn\"><a href=\"#2ff7a095-cf02-485f-bbad-c78473cb1dc3\" id=\"2ff7a095-cf02-485f-bbad-c78473cb1dc3-link\">40<\/a><\/sup> \u2014 including those particularly relevant to professional fields like journalism.<sup data-fn=\"36b560a8-a111-4407-b2fb-d97e0823b295\" class=\"fn\"><a href=\"#36b560a8-a111-4407-b2fb-d97e0823b295\" id=\"36b560a8-a111-4407-b2fb-d97e0823b295-link\">41<\/a><\/sup>&nbsp;These limitations include the tools\u2019 (1) inability to fully handle language ambiguity and cultural nuance, (2) struggle to perform tasks at the level of human experts and (3) biases in outputs based on personal characteristics and attributes present in speech and text data. Evaluating the quality of these tools is challenging in itself: some metrics are overly simplistic, while more holistic methods for evaluating quality or accuracy are opaque and hard to interpret.<sup data-fn=\"1bdee25e-0b19-43d4-b3b5-4996c7a5578c\" class=\"fn\"><a href=\"#1bdee25e-0b19-43d4-b3b5-4996c7a5578c\" id=\"1bdee25e-0b19-43d4-b3b5-4996c7a5578c-link\">42<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI&nbsp;<strong>translation<\/strong>&nbsp;tends to focus on words rather than meaning, but languages have a great deal of typological variation and do not necessarily have parallel sentence structure. Moreover, word-level translation often focuses overly on referential meaning (i.e., what something is about) and lacks attention to indexical meaning (i.e., the social functions of language). Languages do not align one-to-one, with words carrying different formality and\/or emotional meanings; AI tools may use the incorrect word to reflect the perspective of the speaker.<sup data-fn=\"4a50ba01-f94e-4c58-bc87-cf03c7de245f\" class=\"fn\"><a href=\"#4a50ba01-f94e-4c58-bc87-cf03c7de245f\" id=\"4a50ba01-f94e-4c58-bc87-cf03c7de245f-link\">43<\/a><\/sup>&nbsp;For example, professional speech is less formal in English than in Korean<sup data-fn=\"9fb2ca8d-6954-4aef-9071-c4f5bc0f5888\" class=\"fn\"><a href=\"#9fb2ca8d-6954-4aef-9071-c4f5bc0f5888\" id=\"9fb2ca8d-6954-4aef-9071-c4f5bc0f5888-link\">44<\/a><\/sup>&nbsp;or Japanese,<sup data-fn=\"ceadce0d-3baa-423b-a20f-6cf23c2b7865\" class=\"fn\"><a href=\"#ceadce0d-3baa-423b-a20f-6cf23c2b7865\" id=\"ceadce0d-3baa-423b-a20f-6cf23c2b7865-link\">45<\/a><\/sup>&nbsp;and a more literal translation into those two languages will often be socially inappropriate. The outputs these systems produce also change depending on how the translation is described through prompting, such as using specific requests to retain key themes from the source language versus merely asking for a translation into a given language.<sup data-fn=\"40a6366e-a677-4933-b2fa-32e66d2547b8\" class=\"fn\"><a href=\"#40a6366e-a677-4933-b2fa-32e66d2547b8\" id=\"40a6366e-a677-4933-b2fa-32e66d2547b8-link\">46<\/a><\/sup>&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There is also evidence that AI translation models produce results with gender biases<sup data-fn=\"44a65367-ff6a-48cc-8d16-1fcf29e8f84c\" class=\"fn\"><a href=\"#44a65367-ff6a-48cc-8d16-1fcf29e8f84c\" id=\"44a65367-ff6a-48cc-8d16-1fcf29e8f84c-link\">47<\/a><\/sup>&nbsp;(though these biases are diminishing as models improve) by consistently assigning gender to professions (e.g., assuming doctors are men and nurses are women).<sup data-fn=\"84ea23e6-e0bd-485a-810c-abdb7f91b264\" class=\"fn\"><a href=\"#84ea23e6-e0bd-485a-810c-abdb7f91b264\" id=\"84ea23e6-e0bd-485a-810c-abdb7f91b264-link\">48<\/a><\/sup> A review of 133 studies finds that much research on this topic treats gender bias as a purely technical or linguistic problem, rather than examining its social dimensions; these authors also note that most of the studies used machines rather than people to evaluate bias.<sup data-fn=\"f4e42e3f-1af3-48de-9895-269a208de86f\" class=\"fn\"><a href=\"#f4e42e3f-1af3-48de-9895-269a208de86f\" id=\"f4e42e3f-1af3-48de-9895-269a208de86f-link\">49<\/a><\/sup>&nbsp;Given the social nature of bias, the authors raise concerns about this evaluation method. In practice, these studies suggest that journalistic content that is translated without careful review may inadvertently produce outputs that include biased pronouns, occupations or perspectives stemming from the AI tool\u2019s training data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI&nbsp;<strong>transcription<\/strong>&nbsp;tools carry their own limitations, such as a tendency to add content that was never said by the source. This type of error appears more commonly when transcribing speech with longer gaps between words and phrases.<sup data-fn=\"456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f\" class=\"fn\"><a href=\"#456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f\" id=\"456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f-link\">50<\/a><\/sup>&nbsp;Strikingly, there are also critical deficiencies in these tools when transcribing audio from people who speak any form of English besides a fairly narrowly defined set of standard American accents, such as both World Englishes<sup data-fn=\"0ba9bcba-c84b-476f-9f2f-a043f34b1921\" class=\"fn\"><a href=\"#0ba9bcba-c84b-476f-9f2f-a043f34b1921\" id=\"0ba9bcba-c84b-476f-9f2f-a043f34b1921-link\">51<\/a><\/sup>&nbsp;and African American Vernacular English.<sup data-fn=\"006f8f2c-ac56-4bef-a8df-f8153ae9e1a8\" class=\"fn\"><a href=\"#006f8f2c-ac56-4bef-a8df-f8153ae9e1a8\" id=\"006f8f2c-ac56-4bef-a8df-f8153ae9e1a8-link\">52<\/a><\/sup>&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Overall, a recurring theme in the literature reviewed by the working group was that AI tools for transcription and translation of \u201clow-resource\u201d languages are severely lacking.<sup data-fn=\"f7ded5ea-af71-45f2-86db-6af4b98455f8\" class=\"fn\"><a href=\"#f7ded5ea-af71-45f2-86db-6af4b98455f8\" id=\"f7ded5ea-af71-45f2-86db-6af4b98455f8-link\">53<\/a><\/sup>&nbsp;There are significant gaps between human-written and LLM outputs in languages other than English,<sup data-fn=\"0f38f558-9351-4799-a6a5-f11c7322cdc4\" class=\"fn\"><a href=\"#0f38f558-9351-4799-a6a5-f11c7322cdc4\" id=\"0f38f558-9351-4799-a6a5-f11c7322cdc4-link\">54<\/a><\/sup>&nbsp;with journalists in the Global South reporting less confidence in these tools than those in the Global North.<sup data-fn=\"4d076988-685c-4446-a782-1496c6d2e415\" class=\"fn\"><a href=\"#4d076988-685c-4446-a782-1496c6d2e415\" id=\"4d076988-685c-4446-a782-1496c6d2e415-link\">55<\/a><\/sup>&nbsp;Among \u201clow-resource\u201d languages, machine translation for signed languages lags even further behind spoken ones.<sup data-fn=\"ed5c0113-f8d5-46c4-b9e7-756182a42d76\" class=\"fn\"><a href=\"#ed5c0113-f8d5-46c4-b9e7-756182a42d76\" id=\"ed5c0113-f8d5-46c4-b9e7-756182a42d76-link\">56<\/a><\/sup>&nbsp;The most used signed language data sets are small and frequently rely on interpreted data, which is likely to include considerable interference from spoken languages.<sup data-fn=\"2ce2c567-0348-482e-99f0-c33ef291dd26\" class=\"fn\"><a href=\"#2ce2c567-0348-482e-99f0-c33ef291dd26\" id=\"2ce2c567-0348-482e-99f0-c33ef291dd26-link\">57<\/a><\/sup>&nbsp;It is not yet clear how best to represent signed languages computationally, nor how best to evaluate translation between signed and spoken languages.<sup data-fn=\"e76a0da1-fa58-4520-a90d-0ad8a976746d\" class=\"fn\"><a href=\"#e76a0da1-fa58-4520-a90d-0ad8a976746d\" id=\"e76a0da1-fa58-4520-a90d-0ad8a976746d-link\">58<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Although there are many challenges for AI transcription and translation, greater attention is being focused on \u201clow-resource\u201d languages than before. Two prominent examples,&nbsp;<a href=\"https:\/\/www.masakhane.io\/\" target=\"_blank\" rel=\"noreferrer noopener\">Masakhane<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/dailytrust.com\/beyond-the-big-four-enriching-nigerias-ai-language-diversity\/\" target=\"_blank\" rel=\"noreferrer noopener\">Dataphyte<\/a>, seek to improve natural language processing (NLP) research across Africa. Yet more needs to be done, including designing tools for local settings \u2014 particularly in locations that have thus far received less attention from technology companies and AI developers.<\/p>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"margin-bottom:var(--wp--preset--spacing--50);padding-top:var(--wp--preset--spacing--50);padding-right:var(--wp--preset--spacing--50);padding-bottom:var(--wp--preset--spacing--50);padding-left:var(--wp--preset--spacing--50)\">\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-global\"><strong>Global perspectives<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Which languages are in common use vary from country to country, as does the pervasiveness of multilingualism.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Working group members Joshua Olufemi and Oluseyi Olufemi share their perspective:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cThe discussion in this edition centres around three critical issues. First, the limits to the accuracy of existing LLM applications in high-resource languages such as English and Mandarin. The second is the implication of demographic and cultural contexts \u2014 such as accent, parlance, and nuances \u2014 that determine the output of the AI tools. The third relates to local initiatives for innovation and access to data for training AI tools around transcription and translation of low-resource languages.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cIn any case, it is important to expand research and practice beyond just the demand-side effects of journalism\u2019s use of transcription and translation tools. This includes examining supply-side resources, such as Indigenous language content, particularly in broadcast media. In Nigeria, more than 20 Indigenous languages are used in broadcast journalism, representing valuable resources for training AI in low-resource media. Additionally, these languages offer opportunities for media innovation in contexts with limited resources.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-bottom:0px\">\u201cThere is great potential to support both the technological development of AI and media\u2019s practical multilingual reality. What is needed now is collaboration across the board \u2014 including linguists, media and communication practitioners, AI technologists and development policy actors.\u201d<\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-delta-font-size\" id=\"h-where-more\">Where More Research Would Be Helpful<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Day-to-day use:&nbsp;<\/strong>The research does not yet include a deep understanding of where and when journalists are using AI tools for transcription and translation, nor does it explain where and when they would like to once they feel adequate tools are available. What specific issues are the journalists finding? How might these issues be addressed?&nbsp;<\/li>\n\n\n\n<li><strong>Good-enough accuracy:&nbsp;<\/strong>What level of accuracy is good enough for journalistic content?<sup data-fn=\"9668eb6e-11d8-4118-8e61-1187028c12a1\" class=\"fn\"><a href=\"#9668eb6e-11d8-4118-8e61-1187028c12a1\" id=\"9668eb6e-11d8-4118-8e61-1187028c12a1-link\">59<\/a><\/sup>&nbsp;For example, is it acceptable if AI translation tools achieve 95% accuracy when handling text between two languages? The incorrect 5% may provide essential cultural context for audiences in the target language. Research can help inform news organizations and journalism providers to decide what they are comfortable with as current tools are unable to achieve 100% accuracy in every context. Journalism-specific benchmarks for assessing AI transcription and translation tools are also worth developing to address industry-specific needs.<\/li>\n\n\n\n<li><strong>Languages studied:&nbsp;<\/strong>The research on bias in machine translation has been limited to relatively few languages \u2014 with a bias toward written texts \u2014 and has focused primarily on translation into English.<sup data-fn=\"bfec6065-c5f7-49e9-acd3-6d662b5cd1ed\" class=\"fn\"><a href=\"#bfec6065-c5f7-49e9-acd3-6d662b5cd1ed\" id=\"bfec6065-c5f7-49e9-acd3-6d662b5cd1ed-link\">60<\/a><\/sup>&nbsp;It also primarily focuses on gender bias in isolation from other social identities and contexts. Expanding research in this area would help news workers make informed decisions about when AI translation is appropriate and when the risks are too high.<\/li>\n\n\n\n<li><strong>Downstream effects:&nbsp;<\/strong>There is little research about the downstream effects that bias in the tools might have on journalism. For example, if journalists are working under time pressure, are they less likely to interview sources whose voice automated tools do not transcribe as well? Are journalists identifying and editing gender bias in translation tools, or is it impacting their reporting, their audiences\u2019 understanding or both?<\/li>\n\n\n\n<li><strong>Validated data in more languages:&nbsp;<\/strong>If the goal is to use large language models (LLMs) for transcription and translation, considerable attention and effort needs to be placed on building robust LLM training data in non-English and low-resource languages.<sup data-fn=\"d4804da8-bf01-4622-a329-082191968ec2\" class=\"fn\"><a href=\"#d4804da8-bf01-4622-a329-082191968ec2\" id=\"d4804da8-bf01-4622-a329-082191968ec2-link\">61<\/a><\/sup>&nbsp;We need more high-quality translation training data<sup data-fn=\"256d49dd-8fd2-4eee-831e-c03e06b2e6a6\" class=\"fn\"><a href=\"#256d49dd-8fd2-4eee-831e-c03e06b2e6a6\" id=\"256d49dd-8fd2-4eee-831e-c03e06b2e6a6-link\">62<\/a><\/sup>&nbsp;that has been validated in both the source and target languages. Further research should examine how to do this most effectively and efficiently to create inclusive AI models.<\/li>\n\n\n\n<li><strong>Third-party tools:&nbsp;<\/strong>Larger news organizations are developing in-house AI models, particularly adaptations of smaller language models that can be run entirely locally.<sup data-fn=\"75e978b2-5632-46c3-ae10-461e1ff6ff29\" class=\"fn\"><a href=\"#75e978b2-5632-46c3-ae10-461e1ff6ff29\" id=\"75e978b2-5632-46c3-ae10-461e1ff6ff29-link\">63<\/a><\/sup>&nbsp;Meanwhile, many smaller newsrooms are relying on pre-existing models from technology developers. More research needs to consider the potential impacts of relying on third-party tools,<sup data-fn=\"1c12a305-852f-4372-9dfd-086dacb7cafb\" class=\"fn\"><a href=\"#1c12a305-852f-4372-9dfd-086dacb7cafb\" id=\"1c12a305-852f-4372-9dfd-086dacb7cafb-link\">64<\/a><\/sup>&nbsp;and explore how smaller newsrooms can (1) adapt (fine-tune) existing models to better fit their needs and\/or (2) build custom AI tools that are financially viable for them.<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"padding-top:var(--wp--preset--spacing--40);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\n<details class=\"wp-block-mbm-innerblock-accordion is-layout-flow wp-block-mbm-innerblock-accordion-is-layout-flow\">\n<summary class=\"wp-block-mbm-innerblock-accordion-title\">\n<h4 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-echo-font-size\" style=\"margin-right:var(--wp--preset--spacing--50);margin-bottom:0px;line-height:1.25\">Current working group members<\/h4>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--open\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Open accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M17 10.5V15H21.5H22.5V17H21.5H17V21.5V22.5H15V21.5V17H10.5H9.5V15H10.5H15V10.5V9.5H17V10.5Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--closed\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Close accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M22.5 17H21.5H10.5H9.5V15H10.5H21.5H22.5V17Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n<\/summary>\n\n\n\n<div class=\"wp-block-mbm-innerblock-accordion-body\">\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">A list of current working group members and their affiliations is shown here:<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Jaemark Tordecilla<\/strong><br>Independent Media Advisor, Philippines<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Akintunde Babatunde<\/strong><br>Executive Director, Centre for Journalism Innovation and Development<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Claudia B\u00e1ez<\/strong>&nbsp;<br>Associate Consultant, Fathm<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Jay Barchas-Lichtenstein<\/strong><br>Senior Research Manager, Center for News, Technology &amp; Innovation<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Madhav Chinnappa<\/strong><br>Independent Media Consultant<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Utsav Gandhi<\/strong><br>PhD Student, University of Illinois Chicago<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Samuel Jens<\/strong><br>Former Associate Researcher, Center for News, Technology &amp; Innovation<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Amy Mitchell<\/strong><br>Executive Director, Center for News, Technology &amp; Innovation<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Chris Moran<\/strong>&nbsp;<br>Head of Editorial Innovation, Guardian News &amp; Media<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Sophie Morosoli<\/strong><br>Postdoctoral Researcher at the AI, Media &amp; Democracy Lab, University of Amsterdam<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Gary Mundy<\/strong><br>Director Research, Policy and Impact, Thomson Foundation<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Oluwapelumi Oginni<\/strong><br>Project Manager, AI Initiatives, Centre for Journalism Innovation and Development<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Joshua Olufemi<\/strong><br>Executive Director, Dataphyte Foundation<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Oluseyi Olufemi<\/strong><br>Nigeria Country Director, Dataphyte<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Esteban Ponce de Le\u00f3n<\/strong><br>Resident Fellow, Digital Forensic Research Lab (DFRLab) at the Atlantic Council<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Amy Ross Arguedas<\/strong><br>Research Fellow at the Reuters Institute for the Study of Journalism<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Zara Schroeder<\/strong><br>Researcher, Research ICT Africa<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Felix M. Simon<\/strong><br>Research Fellow in AI and News, Reuters Institute for the Study of Journalism &amp; Research Associate, Oxford Internet Institute, University of Oxford<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\"><strong>Scott Timcke<\/strong><br>Senior Research Associate, Research ICT Africa<\/p>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"padding-top:var(--wp--preset--spacing--40);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\n<details class=\"wp-block-mbm-innerblock-accordion is-layout-flow wp-block-mbm-innerblock-accordion-is-layout-flow\">\n<summary class=\"wp-block-mbm-innerblock-accordion-title\">\n<h4 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-echo-font-size\" id=\"h-references\" style=\"margin-right:var(--wp--preset--spacing--50);margin-bottom:0px;line-height:1.25\">References<\/h4>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--open\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Open accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M17 10.5V15H21.5H22.5V17H21.5H17V21.5V22.5H15V21.5V17H10.5H9.5V15H10.5H15V10.5V9.5H17V10.5Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--closed\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Close accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M22.5 17H21.5H10.5H9.5V15H10.5H21.5H22.5V17Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n<\/summary>\n\n\n\n<div class=\"wp-block-mbm-innerblock-accordion-body\">\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Alonso Jim\u00e9nez, E., &amp; Rosado, J. A. (2024). Un an\u00e1lisis del framing de noticias electorales generadas y traducidas mediante inteligencia artificial generativa (ChatGPT-3).&nbsp;<em>Revista Cient\u00edfica de Informaci\u00f3n y Comunicaci\u00f3n<\/em>,&nbsp;<em>21<\/em>, 303\u2013333.<a href=\"https:\/\/doi.org\/10.12795\/IC.2024.I21.14\">&nbsp;https:\/\/doi.org\/10.12795\/IC.2024.I21.14<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Ananny, M., &amp; Pearce, M. (2025, May 12). How We\u2019re Using AI.&nbsp;<em>Columbia Journalism Review<\/em>.<a href=\"https:\/\/www.cjr.org\/feature\/how-were-using-ai-tech-gina-chua-nicholas-thompson-emilia-david-zach-seward-millie-tran.php\">&nbsp;https:\/\/www.cjr.org\/feature\/how-were-using-ai-tech-gina-chua-nicholas-thompson-emilia-david-zach-seward-millie-tran.php<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Asi, N., Fauzan, A., Nugraha, R. F., Binti, J. A. Y. P., &amp; Vanesa, N. (2024). Culturally distinctive features in journalistic text: A case study on students\u2019 vs. AI-generated translations.&nbsp;<em>Yavana Bh\u0101sh\u0101: Journal of English Language Education<\/em>,&nbsp;<em>7<\/em>(1), 54\u201367.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Beckett, C., &amp; Yaseen, M. (2023).&nbsp;<em>Generating Change: A global survey of what news organisations are doing with AI<\/em>. The London School of Economics and Political Science.<a href=\"https:\/\/static1.squarespace.com\/static\/64d60527c01ae7106f2646e9\/t\/656e400a1c23e22da0681e46\/1701724190867\/Generating+Change+_+The+Journalism+AI+report+_+English.pdf\">&nbsp;<\/a><a href=\"https:\/\/www.journalismai.info\/research\/2023-generating-change\">https:\/\/www.journalismai.info\/research\/2023-generating-change<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Brandom, R. (2023, June 7). What languages dominate the internet?&nbsp;<em>Rest of World<\/em>.<a href=\"https:\/\/restofworld.org\/2023\/internet-most-used-languages\/\">&nbsp;https:\/\/restofworld.org\/2023\/internet-most-used-languages\/<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Breaking Language Barriers with AI: Maximizing Accuracy and Efficiency with Machine Translation Technology. (2023).&nbsp;<em>Computer Graphics World<\/em>,&nbsp;<em>46<\/em>(3), 24\u201327.<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Canavilhas, J. (2022). Artificial intelligence applied to journalism: A case study of the \u201cA European Perspective\u201d (UER).&nbsp;<em>Revista Latina de Comunicaci\u00f3n Social<\/em>,&nbsp;<em>80<\/em>, 1\u201316.<a href=\"https:\/\/doi.org\/10.4185\/RLCS-2022-1534\">&nbsp;https:\/\/doi.org\/10.4185\/RLCS-2022-1534<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Caswell, I., &amp; Liang, B. (2020, June 8). Recent Advances in Google Translate.&nbsp;<em>Google Research<\/em>.<a href=\"https:\/\/research.google\/blog\/recent-advances-in-google-translate\/\">&nbsp;https:\/\/research.google\/blog\/recent-advances-in-google-translate\/<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Chan, M. P. Y., Choe, J., Li, A., Chen, Y., Gao, X., &amp; Holliday, N. (2022). Training and typological bias in ASR performance for world Englishes.&nbsp;<em>Interspeech 2022<\/em>, 1273\u20131277.<a href=\"https:\/\/doi.org\/10.21437\/Interspeech.2022-10869\">&nbsp;https:\/\/doi.org\/10.21437\/Interspeech.2022-10869<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Chang, K., Chou, Y.-H., Shi, J., Chen, H.-M., Holliday, N., Scharenborg, O., &amp; Mortensen, D. R. (2024).&nbsp;<em>Self-supervised Speech Representations Still Struggle with African American Vernacular English<\/em>&nbsp;(No. arXiv:2408.14262). arXiv.<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2408.14262\">&nbsp;https:\/\/doi.org\/10.48550\/arXiv.2408.14262<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Court, S., &amp; Elsner, M. (2024).&nbsp;<em>Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem<\/em>. arXiv.<a href=\"https:\/\/doi.org\/10.48550\/ARXIV.2406.15625\">&nbsp;https:\/\/doi.org\/10.48550\/ARXIV.2406.15625<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">De Coster, M., Shterionov, D., Van Herreweghe, M., &amp; Dambre, J. (2024). Machine translation from signed to spoken languages: State of the art and challenges.&nbsp;<em>Universal Access in the Information Society<\/em>,&nbsp;<em>23<\/em>(3), 1305\u20131331.<a href=\"https:\/\/doi.org\/10.1007\/s10209-023-00992-1\">&nbsp;https:\/\/doi.org\/10.1007\/s10209-023-00992-1<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Dubois, D. J., Holliday, N., Waddell, K., &amp; Choffnes, D. (2024). Fair or Fare? Understanding Automated Transcription Error Bias in Social Media and Videoconferencing Platforms.&nbsp;<em>Proceedings of the International AAAI Conference on Web and Social Media<\/em>,&nbsp;<em>18<\/em>(1), 367\u2013380.<a href=\"https:\/\/doi.org\/10.1609\/icwsm.v18i1.31320\">&nbsp;https:\/\/doi.org\/10.1609\/icwsm.v18i1.31320<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Fredrikzon, J. (2025). Rethinking Error: \u201cHallucinations\u201d and Epistemological Indifference.&nbsp;<em>Critical AI<\/em>,&nbsp;<em>3<\/em>(1).<a href=\"https:\/\/doi.org\/10.1215\/2834703X-11700255\">&nbsp;https:\/\/doi.org\/10.1215\/2834703X-11700255<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Frey, C. B., &amp; Llanos-Paredes, P. (2025, March 22).&nbsp;<em>Lost in translation: AI\u2019s impact on translators and foreign language skills<\/em>. CEPR.<a href=\"https:\/\/cepr.org\/voxeu\/columns\/lost-translation-ais-impact-translators-and-foreign-language-skills\">&nbsp;https:\/\/cepr.org\/voxeu\/columns\/lost-translation-ais-impact-translators-and-foreign-language-skills<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Ghosh, S., &amp; Caliskan, A. (2023).&nbsp;<em>ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages<\/em>.<a href=\"https:\/\/doi.org\/10.48550\/ARXIV.2305.10510\">&nbsp;https:\/\/doi.org\/10.48550\/ARXIV.2305.10510<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Gondwe, G. (2025). AI in African Newsrooms: Evaluating Translation Accuracy, Reliability, and Cultural Sensitivity in Tanzanian Media.&nbsp;<em>Journalism Practice<\/em>,&nbsp;<em>0<\/em>(0), 1\u201320.<a href=\"https:\/\/doi.org\/10.1080\/17512786.2025.2507091\">&nbsp;https:\/\/doi.org\/10.1080\/17512786.2025.2507091<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Guo, Y., Conia, S., Zhou, Z., Li, M., Potdar, S., &amp; Xiao, H. (2025).&nbsp;<em>Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs<\/em>&nbsp;(No. arXiv:2410.15956). arXiv.<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2410.15956\">&nbsp;https:\/\/doi.org\/10.48550\/arXiv.2410.15956<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Hagar, N., Cai, M., &amp; Gilbert, J. (2025, September 24). Tiny Tools: A Framework for Human-Centered Technology in Journalism. Generative AI in the Newsroom. https:\/\/generative-ai-newsroom.com\/tiny-tools-a-framework-for-human-centered-technology-in-journalism-e2176dd66cbc<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Howcroft, D. M., &amp; Gkatzia, D. (2022). 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Revue Internationale de La Traduction \/ International Journal of Translation<\/em>,&nbsp;<em>66<\/em>(4\u20135), 829\u2013846.<a href=\"https:\/\/doi.org\/10.1075\/babel.00188.son\">&nbsp;https:\/\/doi.org\/10.1075\/babel.00188.son<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Spencer, C. (2025, November 4).&nbsp;<em>Inside the New Multilingual Newsrooms using GenAI for Translation.<\/em>&nbsp;Generative AI in the Newsroom.&nbsp;<a href=\"https:\/\/generative-ai-newsroom.com\/inside-the-new-multilingual-newsrooms-using-genai-for-translation-4c3b17269811\">https:\/\/generative-ai-newsroom.com\/inside-the-new-multilingual-newsrooms-using-genai-for-translation-4c3b17269811<\/a>&nbsp;<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Tokalac, S. S. (2023, November 28). A translation quality assessment by journalists for journalists.&nbsp;<em>BBC News Labs<\/em>.<a href=\"https:\/\/www.bbc.co.uk\/rdnewslabs\/news\/multilingual-assessment\">&nbsp;https:\/\/www.bbc.co.uk\/rdnewslabs\/news\/multilingual-assessment<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Ullmann, S. (2022). Gender Bias in Machine Translation Systems. In A. Hanemaayer (Ed.),&nbsp;<em>Artificial Intelligence and Its Discontents<\/em>&nbsp;(pp. 123\u2013144). Springer International Publishing.<a href=\"https:\/\/doi.org\/10.1007\/978-3-030-88615-8_7\">&nbsp;https:\/\/doi.org\/10.1007\/978-3-030-88615-8_7<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Valdez Sanabria, A., &amp; Auyanet, S. (2025, July 17). Guarani AI: When building language tech means building community. JournalismAI<em>.<\/em>&nbsp;https:\/\/www.journalismai.info\/blog\/5fcm6ayykhqq7564kbvt9nw92wwmy9<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Vo, L. T. (2025, January 10). Misinformation on TikTok: How Documented Examined Hundreds of<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Videos in Different Languages. Global Investigative Journalism Network. https:\/\/gijn.org\/stories\/tiktok-misinformation-how-documented-translated-hundreds-videos\/<\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">W3Techs. (n.d.).&nbsp;<em>Usage Statistics of Content Languages for Websites, October 2025<\/em>. Retrieved October 22, 2025, from<a href=\"https:\/\/w3techs.com\/technologies\/overview\/content_language\">&nbsp;https:\/\/w3techs.com\/technologies\/overview\/content_language<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Wang, H. (2022). Short Sequence Chinese-English Machine Translation Based on Generative Adversarial Networks of Emotion.&nbsp;<em>Computational Intelligence and Neuroscience<\/em>,&nbsp;<em>2022<\/em>, 1\u201310.<a href=\"https:\/\/doi.org\/10.1155\/2022\/3385477\">&nbsp;https:\/\/doi.org\/10.1155\/2022\/3385477<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Wolfe, R., Braffort, A., Efthimiou, E., Fotinea, E., Hanke, T., &amp; Shterionov, D. (2025). Special issue on sign language translation and avatar technology.&nbsp;<em>Universal Access in the Information Society<\/em>,&nbsp;<em>24<\/em>(1), 1\u20133.<a href=\"https:\/\/doi.org\/10.1007\/s10209-023-01014-w\">&nbsp;https:\/\/doi.org\/10.1007\/s10209-023-01014-w<\/a><\/p>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Yan, J., Yan, P., Chen, Y., Li, J., Zhu, X., &amp; Zhang, Y. (2024).&nbsp;<em>Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels<\/em>. arXiv.<a href=\"https:\/\/doi.org\/10.48550\/ARXIV.2411.13775\">&nbsp;https:\/\/doi.org\/10.48550\/ARXIV.2411.13775<\/a><\/p>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n<div class=\"wp-block-gridible-responsive-spacer\" style=\"height:32px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-group has-shade-4-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"padding-top:var(--wp--preset--spacing--40);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\n<details class=\"wp-block-mbm-innerblock-accordion is-layout-flow wp-block-mbm-innerblock-accordion-is-layout-flow\">\n<summary class=\"wp-block-mbm-innerblock-accordion-title\">\n<h4 class=\"wp-block-heading has-aktiv-grotesk-condensed-font-family has-echo-font-size\" style=\"margin-right:var(--wp--preset--spacing--50);margin-bottom:0px;line-height:1.25\">Appendix<\/h4>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--open\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Open accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M17 10.5V15H21.5H22.5V17H21.5H17V21.5V22.5H15V21.5V17H10.5H9.5V15H10.5H15V10.5V9.5H17V10.5Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n\n\n\n<div class=\"wp-block-outermost-icon-block wp-block-mbm-innerblock-accordion-title__icon--closed\"><div class=\"icon-container\" style=\"width:32px;transform:rotate(0deg) scaleX(1) scaleY(1)\"><svg width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-label=\"Close accordion\"><rect width=\"32\" height=\"32\" rx=\"16\" fill=\"black\"><\/rect><path d=\"M22.5 17H21.5H10.5H9.5V15H10.5H21.5H22.5V17Z\" fill=\"#F9F6F2\"><\/path><\/svg><\/div><\/div>\n<\/summary>\n\n\n\n<div class=\"wp-block-mbm-innerblock-accordion-body\">\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Works referenced for AI transcription and translation<br><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Paper<\/strong><\/td><td><strong>Focus<\/strong><\/td><td><strong>Scope<\/strong><\/td><\/tr><tr><td>Alonso Jim\u00e9nez &amp; Rosado, 2024<\/td><td>Translation<\/td><td>Creation of 68 Spanish-language political articles using ChatGPT-3 and translated into English.<\/td><\/tr><tr><td>Asi et al., 2024<\/td><td>Translation<\/td><td>Examination of how ChatGPT 3.5 and DeepL compare to student translators across six texts.<\/td><\/tr><tr><td>Beckett &amp; Yaseen, 2023<\/td><td>Both<\/td><td>Survey of 105 news organizations from 46 countries as well as interviews with journalists and newsroom staff.<\/td><\/tr><tr><td>Canavilhas, 2022<\/td><td>Translation<\/td><td>Case study of the \u201cA European Perspective\u201d project with further analysis of 54 news items from the website RTP- R\u00e1dio Televis\u00e3o Portuguesa.<\/td><\/tr><tr><td>Chan et al., 2022<\/td><td>Transcription<\/td><td>Examination of the accuracy of Otter.ai across 24 World Englishes (<em>n&nbsp;<\/em>= 1,227 recordings).<\/td><\/tr><tr><td>Chang et al., 2024<\/td><td>Transcription<\/td><td>Comparison of the accuracy of semi-supervised learning models on Mainstream American English (3.33 hours of recordings) and African American Vernacular English using (19.39 hours of recordings).<\/td><\/tr><tr><td>Court &amp; Elsner, 2024<\/td><td>Translation<\/td><td>Translation experiments with 50 pairs of Spanish-Quechua (Indigenous Peruvian language) using GPT-3.5 turbo, GPT-4o, Gemini 1.5 Pro and Llama 3.<\/td><\/tr><tr><td>De Coster et al., 2024<\/td><td>Translation<\/td><td>Review on machine translation from signed to spoken languages.<\/td><\/tr><tr><td>Dubois et al., 2024<\/td><td>Transcription<\/td><td>Examination of the accuracy of seven transcription tools (e.g., YouTube, Facebook Video, Microsoft Stream, Zoom, BlueJeans, Webex and Google Meet) using 846 TED talk speakers (194 hours of content).<\/td><\/tr><tr><td>Fredrikzon, 2025<\/td><td>Other<\/td><td>Thought article about AI \u201challucinations\u201d and mistakes.<\/td><\/tr><tr><td>Frey &amp; Llanos-Paredes, 2025<\/td><td>Translation<\/td><td>Examination of U.S. data of Google Translate search data, translator job postings and local wage and employment stats from 2010 to 2023.<\/td><\/tr><tr><td>Ghosh &amp; Caliskan, 2023<\/td><td>Translation<\/td><td>Assessment of GPT performance of 50 occupations from English to Bengali, Farsi, Malay, Tagalog, Thai and Turkish.&nbsp;<\/td><\/tr><tr><td>Gondwe, 2025<\/td><td>Translation<\/td><td>Case study in Tanzania of 19 news organizations and interviews with 38 news editors.<\/td><\/tr><tr><td>Guo et al., 2025<\/td><td>Translation<\/td><td>Comparison of AI models (Llama, Qwen and Mistral) for English, Chinese and French translations using 3,722 Wikipedia entries.<\/td><\/tr><tr><td>Hagar et al., 2025<\/td><td>Other<\/td><td>Framework for using small language models in the newsroom<\/td><\/tr><tr><td>Howcroft &amp; Gkatzia, 2022<\/td><td>Other<\/td><td>Overview of natural language generation approaches for low-resource languages.<\/td><\/tr><tr><td>Kocmi et al., 2024<\/td><td>Translation<\/td><td>Results of 11 language pair translations from 28 participants\u2019 models in addition to 8 LLMs and 4 online translation providers.<\/td><\/tr><tr><td>Kocmi et al., 2025<\/td><td>Translation<\/td><td>Preliminary results of 32 language pair translations from 36 participants\u2019 models.<\/td><\/tr><tr><td>Koenecke et al., 2024<\/td><td>Transcription<\/td><td>Analysis of Whisper transcription \u201challucinations\u201d in English (<em>n&nbsp;<\/em>= 187 audio segments).<\/td><\/tr><tr><td>Lee, 2024<\/td><td>Translation<\/td><td>Overview of machine translation technologies and how they compare to human translators.<\/td><\/tr><tr><td>Leiter et al., 2024<\/td><td>Translation<\/td><td>Concept paper identifying key properties of machine translation metrics.<\/td><\/tr><tr><td>Levit et al., 2017<\/td><td>Transcription<\/td><td>Development of a crowdsourcing approach that includes automatic speech recognition and human graders for building transcription data.<\/td><\/tr><tr><td>Moghe et al., 2025<\/td><td>Translation<\/td><td>Presentation of a new accuracy metric for AI translation using 36,000+ examples across 146 language pairs.<\/td><\/tr><tr><td>Moneus &amp; Sahari, 2024<\/td><td>Translation<\/td><td>Comparison of 10 professional translators with three AI tools (ChatSonic, Bing Chat, and ChatGPT-4) on six legal texts in Arabic and English.<\/td><\/tr><tr><td>Munoriyarwa et al., 2023<\/td><td>Translation<\/td><td>Semi-structured interviews with South African journalists from six news organizations.<\/td><\/tr><tr><td>Noll et al., 2025<\/td><td>Translation<\/td><td>Results of medical experts grading translations with ChatGPT and DeepL of 120 medical terms and 180 synonyms from English to German.<\/td><\/tr><tr><td>Novytska et al., 2025<\/td><td>Translation<\/td><td>Synthesis of research on audiovisual translation with a specific focus on subtitles.<\/td><\/tr><tr><td>Ojewale et al., 2025<\/td><td>Translation<\/td><td>Examination of functional multi-lingual model performance for translating two datasets in English into French, Spanish, Hindi, Arabic and Yoruba.<\/td><\/tr><tr><td>Ojo et al., 2025<\/td><td>Translation<\/td><td>Development of a large-scale LLM evaluation benchmark called AfroBench which includes 15 tasks, 22 datasets and 64 indigenous African languages.<\/td><\/tr><tr><td>Pava et al., 2025<\/td><td>Other<\/td><td>White paper on approaches to building data resources for \u201clow-resource\u201d languages.<\/td><\/tr><tr><td>Prates et al., 2020<\/td><td>Translation<\/td><td>Assessment of gender bias using a list of occupations (<em>n<\/em>&nbsp;= 1,019) and translating these occupations in 14 languages into English.<\/td><\/tr><tr><td>Qingliang, 2024<\/td><td>Translation<\/td><td>Summary of translator and AI research and potential future developments.<\/td><\/tr><tr><td>Ross Arguedas, 2024<\/td><td>Both<\/td><td>Broad study about public attitudes towards AI uses in journalism, including transcription and translation.<\/td><\/tr><tr><td>Savoldi et al., 2025<\/td><td>Translation<\/td><td>Examination of 133 papers published between 2016 and December 2024 on the topic of gender bias in automatic (machine) translation.<\/td><\/tr><tr><td>Schellmann, 2025<\/td><td>Transcription<\/td><td>Study employing four chatbots (ChatGPT-4o, Opus 4, Perplexity Pro, Gemini 2.5 Pro) to test transcription of local government meetings in Clayton County, GA; Cleveland, OH; and Long Beach, NY.<\/td><\/tr><tr><td>Shahmerdanova, 2025<\/td><td>Translation<\/td><td>Review article of AI and translation research.<\/td><\/tr><tr><td>Simon, 2025<\/td><td>Both<\/td><td>Broad study of AI use in the journalism industry and how it impacts industry gatekeeping.<\/td><\/tr><tr><td>Simon &amp; Isaza-Ibarra, 2023<\/td><td>Both<\/td><td>Summary of how AI is being used and integrated in the journalism industry.<\/td><\/tr><tr><td>Simon et al., 2025<\/td><td>Both<\/td><td>Broad study about the public\u2019s attitudes towards AI in journalism.<\/td><\/tr><tr><td>Song, 2020<\/td><td>Translation<\/td><td>Examination of 188 news stories from March 2001 to March 2019 and compared official newspaper translations to three machine translation tools (Google Translate, Papago and Kakao).<\/td><\/tr><tr><td>Tokalac, 2023<\/td><td>Both<\/td><td>Study in which journalist-evaluators perform three tasks: (1) transcriptions in their language, (2) English translation into their language and (3) translating their language into English to test model performance.<\/td><\/tr><tr><td>Ullmann, 2022<\/td><td>Translation<\/td><td>Summary of existing literature on gender bias in AI translation.<\/td><\/tr><tr><td>Wang, 2022<\/td><td>Translation<\/td><td>Development of a novel neural machine translation model that uses a generative adversarial network (GAN) and tests using 1M English to Chinese sentences.<\/td><\/tr><tr><td>Wolfe et al., 2025<\/td><td>Translation<\/td><td>Introduction to a special issue on machine translation for signed languages.<\/td><\/tr><tr><td>Yan et al., 2024<\/td><td>Translation<\/td><td>Analysis comparing ChatGPT-4 to three levels of human translator expertise across three language pairs: Chinese-English, Russian-English, Chinese-Hindi.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-aktiv-grotesk-font-family has-foxtrot-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0px\">Not included: 15 resources providing background information about AI transcription and translation, most of which were news articles (Ananny &amp; Pearce, 2025; Brandom, 2023; Caswell &amp; Liang, 2020; Jarnow, 2017; Judah, 2025; Kahn, 2025; Langer, 2025; Newman &amp; Cherubini, 2025; Ohumu, 2025; Okolo &amp; Tano, 2025; Spencer, 2025; Valdez Sanabria &amp; Auyanet, 2025; Vo, 2025; W3Techs n.d.; Breaking Language Barriers with AI, 2023).<\/p>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-text-color has-shade-2-color has-alpha-channel-opacity has-shade-2-background-color has-background\" style=\"margin-top:var(--wp--preset--spacing--50);margin-bottom:var(--wp--preset--spacing--50)\"\/>\n\n\n\n<p class=\"has-aktiv-grotesk-condensed-font-family has-foxtrot-font-size wp-block-paragraph\"><strong>Footnotes<\/strong><\/p>\n\n\n<ol class=\"wp-block-footnotes\"><li id=\"29d91891-463f-43ac-80ab-b581d53f073a\">Mari, 2024 <a href=\"#29d91891-463f-43ac-80ab-b581d53f073a-link\" aria-label=\"Jump to footnote reference 1\">\u21a9\ufe0e<\/a><\/li><li id=\"80797874-c08d-485c-8360-3ebd232eeb08\">Ananny &amp; Pearce, 2025; Beckett &amp; Yaseen, 2023; Gondwe, 2025; Kahn, 2025; Munoriyarwa et al., 2023; Simon &amp; Isaza-Ibarra, 2023 <a href=\"#80797874-c08d-485c-8360-3ebd232eeb08-link\" aria-label=\"Jump to footnote reference 2\">\u21a9\ufe0e<\/a><\/li><li id=\"199eccc4-97cd-42e4-87d7-3da827d6c1e8\">Qingliang; 2024; Shahmerdanova, 2025; Tokalac, 2023 <a href=\"#199eccc4-97cd-42e4-87d7-3da827d6c1e8-link\" aria-label=\"Jump to footnote reference 3\">\u21a9\ufe0e<\/a><\/li><li id=\"a78f92c3-ce43-4917-a1df-dc50ad787328\">Spencer, 2025 <a href=\"#a78f92c3-ce43-4917-a1df-dc50ad787328-link\" aria-label=\"Jump to footnote reference 4\">\u21a9\ufe0e<\/a><\/li><li id=\"b1fcb2fd-e7bd-4632-957a-2b792987c69e\">Court &amp; Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025; Pava et al., 2025; Moghe et al., 2025 <a href=\"#b1fcb2fd-e7bd-4632-957a-2b792987c69e-link\" aria-label=\"Jump to footnote reference 5\">\u21a9\ufe0e<\/a><\/li><li id=\"833de0f1-bc75-4c12-afea-78e42d298801\">Chan et al., 2022; Chang et al., 2024 <a href=\"#833de0f1-bc75-4c12-afea-78e42d298801-link\" aria-label=\"Jump to footnote reference 6\">\u21a9\ufe0e<\/a><\/li><li id=\"621b7148-c218-4ef2-9fed-8e348a66c6d8\">Ghosh &amp; Caliskan, 2023; Prates et al., 2020; Savoldi et al., 2025; Ullmann, 2022 <a href=\"#621b7148-c218-4ef2-9fed-8e348a66c6d8-link\" aria-label=\"Jump to footnote reference 7\">\u21a9\ufe0e<\/a><\/li><li id=\"6a102a13-4fac-4a52-be84-c14155d92e05\">Fredrikzon, 2025 <a href=\"#6a102a13-4fac-4a52-be84-c14155d92e05-link\" aria-label=\"Jump to footnote reference 8\">\u21a9\ufe0e<\/a><\/li><li id=\"01d27cb9-1f34-4a73-b3bf-fac1f54cf49e\">Dubois et al., 2024 <a href=\"#01d27cb9-1f34-4a73-b3bf-fac1f54cf49e-link\" aria-label=\"Jump to footnote reference 9\">\u21a9\ufe0e<\/a><\/li><li id=\"21e0a365-4f3e-4b3a-a245-e6c249bba61d\">Jarnow, 2017 <a href=\"#21e0a365-4f3e-4b3a-a245-e6c249bba61d-link\" aria-label=\"Jump to footnote reference 10\">\u21a9\ufe0e<\/a><\/li><li id=\"336865ee-1683-451b-a785-1ac7bb9134c7\">Frey &amp; Llanos-Paredes, 2025 <a href=\"#336865ee-1683-451b-a785-1ac7bb9134c7-link\" aria-label=\"Jump to footnote reference 11\">\u21a9\ufe0e<\/a><\/li><li id=\"ef6fd1d3-d1cf-47be-8869-b52141ec2696\">Caswell &amp; Liang, 2020 <a href=\"#ef6fd1d3-d1cf-47be-8869-b52141ec2696-link\" aria-label=\"Jump to footnote reference 12\">\u21a9\ufe0e<\/a><\/li><li id=\"65c32dbe-12aa-4017-bd18-ded2abdf5db4\">Chan et al., 2022; Chang et al., 2024 <a href=\"#65c32dbe-12aa-4017-bd18-ded2abdf5db4-link\" aria-label=\"Jump to footnote reference 13\">\u21a9\ufe0e<\/a><\/li><li id=\"fc15492a-4515-4f56-8403-af9083c04d75\">Court &amp; Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025 <a href=\"#fc15492a-4515-4f56-8403-af9083c04d75-link\" aria-label=\"Jump to footnote reference 14\">\u21a9\ufe0e<\/a><\/li><li id=\"d1388023-9e20-4bf9-9ada-a222e545ee02\">W3Techs, n.d. <a href=\"#d1388023-9e20-4bf9-9ada-a222e545ee02-link\" aria-label=\"Jump to footnote reference 15\">\u21a9\ufe0e<\/a><\/li><li id=\"7e516919-7202-43c7-b1e4-4230e84d8dac\">Brandom, 2023 <a href=\"#7e516919-7202-43c7-b1e4-4230e84d8dac-link\" aria-label=\"Jump to footnote reference 16\">\u21a9\ufe0e<\/a><\/li><li id=\"54d5d914-660f-40c0-ba61-52ee5ee74fec\">Orimemi, 2025 <a href=\"#54d5d914-660f-40c0-ba61-52ee5ee74fec-link\" aria-label=\"Jump to footnote reference 17\">\u21a9\ufe0e<\/a><\/li><li id=\"23ae2852-56d5-48b3-bb99-25d1675f7754\">Judah, 2025 <a href=\"#23ae2852-56d5-48b3-bb99-25d1675f7754-link\" aria-label=\"Jump to footnote reference 18\">\u21a9\ufe0e<\/a><\/li><li id=\"6734b9e8-3840-4537-b1e2-6ff14d8284a1\">Ananny &amp; Pearce, 2025; Beckett &amp; Yaseen, 2023; Simon &amp; Isaza-Ibarra, 2023; Canavilhas, 2022; Ohumu, 2025; Simon, 2025 <a href=\"#6734b9e8-3840-4537-b1e2-6ff14d8284a1-link\" aria-label=\"Jump to footnote reference 19\">\u21a9\ufe0e<\/a><\/li><li id=\"6f4c4029-6d87-467d-9edc-9147e53fd199\">Langer, 2025 <a href=\"#6f4c4029-6d87-467d-9edc-9147e53fd199-link\" aria-label=\"Jump to footnote reference 20\">\u21a9\ufe0e<\/a><\/li><li id=\"cd18678a-1784-4761-ba39-7988ed05ecd2\">Canavilhas, 2022 <a href=\"#cd18678a-1784-4761-ba39-7988ed05ecd2-link\" aria-label=\"Jump to footnote reference 21\">\u21a9\ufe0e<\/a><\/li><li id=\"33f2991b-81b7-4b31-82a9-ce590959530c\">Ohumu, 2025. See Vo 2025 for another example. <a href=\"#33f2991b-81b7-4b31-82a9-ce590959530c-link\" aria-label=\"Jump to footnote reference 22\">\u21a9\ufe0e<\/a><\/li><li id=\"9ed420a3-b60c-47c3-99aa-f7adbdda0ffd\">Valdez Sanabria &amp; Auyanet, 2025 <a href=\"#9ed420a3-b60c-47c3-99aa-f7adbdda0ffd-link\" aria-label=\"Jump to footnote reference 23\">\u21a9\ufe0e<\/a><\/li><li id=\"8d065a39-271e-4932-9b7e-e47ff50491f0\">Munoriyarwa et al., 2023 <a href=\"#8d065a39-271e-4932-9b7e-e47ff50491f0-link\" aria-label=\"Jump to footnote reference 24\">\u21a9\ufe0e<\/a><\/li><li id=\"52871edc-dcac-49d9-aadd-ebd5c02aa7a1\">Beckett &amp; Yaseen, 2023; Munoriyarwa et al., 2023; Kahn, 2025 <a href=\"#52871edc-dcac-49d9-aadd-ebd5c02aa7a1-link\" aria-label=\"Jump to footnote reference 25\">\u21a9\ufe0e<\/a><\/li><li id=\"2642a422-56a4-4dec-be29-cf8479fc2761\">Gondwe, 2025 <a href=\"#2642a422-56a4-4dec-be29-cf8479fc2761-link\" aria-label=\"Jump to footnote reference 26\">\u21a9\ufe0e<\/a><\/li><li id=\"11aff7b0-73bf-4624-90c2-b33a147fa290\">Tokalac, 2023 <a href=\"#11aff7b0-73bf-4624-90c2-b33a147fa290-link\" aria-label=\"Jump to footnote reference 27\">\u21a9\ufe0e<\/a><\/li><li id=\"753cfebc-0969-4ed7-9c34-bda719695d92\">Shahmerdanova, 2025 <a href=\"#753cfebc-0969-4ed7-9c34-bda719695d92-link\" aria-label=\"Jump to footnote reference 28\">\u21a9\ufe0e<\/a><\/li><li id=\"5e701cd0-1e7f-49e2-b074-5c77d07f1459\">Newman &amp; Cherubini, 2025; Ross Arguedas, 2025 <a href=\"#5e701cd0-1e7f-49e2-b074-5c77d07f1459-link\" aria-label=\"Jump to footnote reference 29\">\u21a9\ufe0e<\/a><\/li><li id=\"3509cb94-90b7-4508-9d96-35bcfb49c746\">Ross Arguedas, 2025 <a href=\"#3509cb94-90b7-4508-9d96-35bcfb49c746-link\" aria-label=\"Jump to footnote reference 30\">\u21a9\ufe0e<\/a><\/li><li id=\"b920bdde-ccc9-4518-9d5a-bc533114acf1\">Ross Arguedas, 2025 <a href=\"#b920bdde-ccc9-4518-9d5a-bc533114acf1-link\" aria-label=\"Jump to footnote reference 31\">\u21a9\ufe0e<\/a><\/li><li id=\"6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0\">Simon et al., 2025 <a href=\"#6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0-link\" aria-label=\"Jump to footnote reference 32\">\u21a9\ufe0e<\/a><\/li><li id=\"e563f6f6-a7a5-458d-8c24-921d72436b69\">Breaking Language Barriers with AI: Maximizing Accuracy and Efficiency with Machine Translation Technology, 2023 <a href=\"#e563f6f6-a7a5-458d-8c24-921d72436b69-link\" aria-label=\"Jump to footnote reference 33\">\u21a9\ufe0e<\/a><\/li><li id=\"00442057-afbf-4350-b456-6d338f1f0403\">Alonso Jim\u00e9nez &amp; Rosado, 2024 <a href=\"#00442057-afbf-4350-b456-6d338f1f0403-link\" aria-label=\"Jump to footnote reference 34\">\u21a9\ufe0e<\/a><\/li><li id=\"36cae93d-2512-48ae-89ac-9a10f7c3c3e3\">Noll et al., 2025 <a href=\"#36cae93d-2512-48ae-89ac-9a10f7c3c3e3-link\" aria-label=\"Jump to footnote reference 35\">\u21a9\ufe0e<\/a><\/li><li id=\"0bd57973-aa4a-4949-a865-c2ca4f3672c0\">Moneus &amp; Sahari, 2024 <a href=\"#0bd57973-aa4a-4949-a865-c2ca4f3672c0-link\" aria-label=\"Jump to footnote reference 36\">\u21a9\ufe0e<\/a><\/li><li id=\"a78df0d2-5c1e-461b-90cd-390c07838ad1\">Asi et al., 2024 <a href=\"#a78df0d2-5c1e-461b-90cd-390c07838ad1-link\" aria-label=\"Jump to footnote reference 37\">\u21a9\ufe0e<\/a><\/li><li id=\"c7d859e3-e24d-466d-95cc-d6c3e5604228\">Langer, 2025; Schellmann, 2025 <a href=\"#c7d859e3-e24d-466d-95cc-d6c3e5604228-link\" aria-label=\"Jump to footnote reference 38\">\u21a9\ufe0e<\/a><\/li><li id=\"3bb91335-2924-4562-a784-9ac17a6dc44e\">Guo et al., 2025; Kocmi et al., 2024; Kocmi et al. 2025; Levit et al., 2017; Moghe et al. 2025; Wang, 2022 <a href=\"#3bb91335-2924-4562-a784-9ac17a6dc44e-link\" aria-label=\"Jump to footnote reference 39\">\u21a9\ufe0e<\/a><\/li><li id=\"2ff7a095-cf02-485f-bbad-c78473cb1dc3\">Lee, 2024; Novytska et al., 2025; Yan et al., 2024 <a href=\"#2ff7a095-cf02-485f-bbad-c78473cb1dc3-link\" aria-label=\"Jump to footnote reference 40\">\u21a9\ufe0e<\/a><\/li><li id=\"36b560a8-a111-4407-b2fb-d97e0823b295\">Schellmann, 2025; Song, 2020 <a href=\"#36b560a8-a111-4407-b2fb-d97e0823b295-link\" aria-label=\"Jump to footnote reference 41\">\u21a9\ufe0e<\/a><\/li><li id=\"1bdee25e-0b19-43d4-b3b5-4996c7a5578c\">Leiter et al., 2024 <a href=\"#1bdee25e-0b19-43d4-b3b5-4996c7a5578c-link\" aria-label=\"Jump to footnote reference 42\">\u21a9\ufe0e<\/a><\/li><li id=\"4a50ba01-f94e-4c58-bc87-cf03c7de245f\">Song, 2020 <a href=\"#4a50ba01-f94e-4c58-bc87-cf03c7de245f-link\" aria-label=\"Jump to footnote reference 43\">\u21a9\ufe0e<\/a><\/li><li id=\"9fb2ca8d-6954-4aef-9071-c4f5bc0f5888\">Song, 2020 <a href=\"#9fb2ca8d-6954-4aef-9071-c4f5bc0f5888-link\" aria-label=\"Jump to footnote reference 44\">\u21a9\ufe0e<\/a><\/li><li id=\"ceadce0d-3baa-423b-a20f-6cf23c2b7865\">Lee, 2024 <a href=\"#ceadce0d-3baa-423b-a20f-6cf23c2b7865-link\" aria-label=\"Jump to footnote reference 45\">\u21a9\ufe0e<\/a><\/li><li id=\"40a6366e-a677-4933-b2fa-32e66d2547b8\">Lee, 2024 <a href=\"#40a6366e-a677-4933-b2fa-32e66d2547b8-link\" aria-label=\"Jump to footnote reference 46\">\u21a9\ufe0e<\/a><\/li><li id=\"44a65367-ff6a-48cc-8d16-1fcf29e8f84c\">Savoldi et al., 2025; Ullmann, 2022 <a href=\"#44a65367-ff6a-48cc-8d16-1fcf29e8f84c-link\" aria-label=\"Jump to footnote reference 47\">\u21a9\ufe0e<\/a><\/li><li id=\"84ea23e6-e0bd-485a-810c-abdb7f91b264\">Ghosh &amp; Caliskan, 2023; Prates et al., 2020 <a href=\"#84ea23e6-e0bd-485a-810c-abdb7f91b264-link\" aria-label=\"Jump to footnote reference 48\">\u21a9\ufe0e<\/a><\/li><li id=\"f4e42e3f-1af3-48de-9895-269a208de86f\">Savoldi et al., 2025 <a href=\"#f4e42e3f-1af3-48de-9895-269a208de86f-link\" aria-label=\"Jump to footnote reference 49\">\u21a9\ufe0e<\/a><\/li><li id=\"456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f\">Koenecke et al., 2024; Schellmann, 2025 <a href=\"#456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f-link\" aria-label=\"Jump to footnote reference 50\">\u21a9\ufe0e<\/a><\/li><li id=\"0ba9bcba-c84b-476f-9f2f-a043f34b1921\">Chan et al., 2022 <a href=\"#0ba9bcba-c84b-476f-9f2f-a043f34b1921-link\" aria-label=\"Jump to footnote reference 51\">\u21a9\ufe0e<\/a><\/li><li id=\"006f8f2c-ac56-4bef-a8df-f8153ae9e1a8\">Chang et al., 2024 <a href=\"#006f8f2c-ac56-4bef-a8df-f8153ae9e1a8-link\" aria-label=\"Jump to footnote reference 52\">\u21a9\ufe0e<\/a><\/li><li id=\"f7ded5ea-af71-45f2-86db-6af4b98455f8\">Ojewale et al., 2025; Ojo et al., 2025; Pava et al., 2025 <a href=\"#f7ded5ea-af71-45f2-86db-6af4b98455f8-link\" aria-label=\"Jump to footnote reference 53\">\u21a9\ufe0e<\/a><\/li><li id=\"0f38f558-9351-4799-a6a5-f11c7322cdc4\">Guo et al., 2025 <a href=\"#0f38f558-9351-4799-a6a5-f11c7322cdc4-link\" aria-label=\"Jump to footnote reference 54\">\u21a9\ufe0e<\/a><\/li><li id=\"4d076988-685c-4446-a782-1496c6d2e415\">Beckett &amp; Yaseen, 2023 <a href=\"#4d076988-685c-4446-a782-1496c6d2e415-link\" aria-label=\"Jump to footnote reference 55\">\u21a9\ufe0e<\/a><\/li><li id=\"ed5c0113-f8d5-46c4-b9e7-756182a42d76\">Wolfe et al., 2025 <a href=\"#ed5c0113-f8d5-46c4-b9e7-756182a42d76-link\" aria-label=\"Jump to footnote reference 56\">\u21a9\ufe0e<\/a><\/li><li id=\"2ce2c567-0348-482e-99f0-c33ef291dd26\">De Coster et al., 2024 <a href=\"#2ce2c567-0348-482e-99f0-c33ef291dd26-link\" aria-label=\"Jump to footnote reference 57\">\u21a9\ufe0e<\/a><\/li><li id=\"e76a0da1-fa58-4520-a90d-0ad8a976746d\">De Coster et al., 2024 <a href=\"#e76a0da1-fa58-4520-a90d-0ad8a976746d-link\" aria-label=\"Jump to footnote reference 58\">\u21a9\ufe0e<\/a><\/li><li id=\"9668eb6e-11d8-4118-8e61-1187028c12a1\">How best to evaluate translation is largely out of scope, but see Leiter 2024. <a href=\"#9668eb6e-11d8-4118-8e61-1187028c12a1-link\" aria-label=\"Jump to footnote reference 59\">\u21a9\ufe0e<\/a><\/li><li id=\"bfec6065-c5f7-49e9-acd3-6d662b5cd1ed\">Savoldi et al., 2025 <a href=\"#bfec6065-c5f7-49e9-acd3-6d662b5cd1ed-link\" aria-label=\"Jump to footnote reference 60\">\u21a9\ufe0e<\/a><\/li><li id=\"d4804da8-bf01-4622-a329-082191968ec2\">Howcroft &amp; Gkatzia, 2022 <a href=\"#d4804da8-bf01-4622-a329-082191968ec2-link\" aria-label=\"Jump to footnote reference 61\">\u21a9\ufe0e<\/a><\/li><li id=\"256d49dd-8fd2-4eee-831e-c03e06b2e6a6\">Qingliang, 2024 <a href=\"#256d49dd-8fd2-4eee-831e-c03e06b2e6a6-link\" aria-label=\"Jump to footnote reference 62\">\u21a9\ufe0e<\/a><\/li><li id=\"75e978b2-5632-46c3-ae10-461e1ff6ff29\">Hagar et al., 2025 <a href=\"#75e978b2-5632-46c3-ae10-461e1ff6ff29-link\" aria-label=\"Jump to footnote reference 63\">\u21a9\ufe0e<\/a><\/li><li id=\"1c12a305-852f-4372-9dfd-086dacb7cafb\">Simon, 2024 <a href=\"#1c12a305-852f-4372-9dfd-086dacb7cafb-link\" aria-label=\"Jump to footnote reference 64\">\u21a9\ufe0e<\/a><\/li><\/ol>","protected":false},"excerpt":{"rendered":"<p>The second briefing from the AI and Journalism Research Working Group finds that while journalists are using AI transcription and translation systems, accuracy and accessibility vary, making continued human oversight essential.<\/p>\n","protected":false},"author":3,"featured_media":9438,"parent":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mbm_post_meta_color_slug":[],"_mbm_featured_category_id":"76","inline_featured_image":false,"_mbmamp_team_author_id":[],"footnotes":"[{\"content\":\"Mari, 2024\",\"id\":\"29d91891-463f-43ac-80ab-b581d53f073a\"},{\"content\":\"Ananny &amp; Pearce, 2025; Beckett &amp; Yaseen, 2023; Gondwe, 2025; Kahn, 2025; Munoriyarwa et al., 2023; Simon &amp; Isaza-Ibarra, 2023\",\"id\":\"80797874-c08d-485c-8360-3ebd232eeb08\"},{\"content\":\"Qingliang; 2024; Shahmerdanova, 2025; Tokalac, 2023\",\"id\":\"199eccc4-97cd-42e4-87d7-3da827d6c1e8\"},{\"content\":\"Spencer, 2025\",\"id\":\"a78f92c3-ce43-4917-a1df-dc50ad787328\"},{\"content\":\"Court &amp; Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025; Pava et al., 2025; Moghe et al., 2025\",\"id\":\"b1fcb2fd-e7bd-4632-957a-2b792987c69e\"},{\"content\":\"Chan et al., 2022; Chang et al., 2024\",\"id\":\"833de0f1-bc75-4c12-afea-78e42d298801\"},{\"content\":\"Ghosh &amp; Caliskan, 2023; Prates et al., 2020; Savoldi et al., 2025; Ullmann, 2022\",\"id\":\"621b7148-c218-4ef2-9fed-8e348a66c6d8\"},{\"content\":\"Fredrikzon, 2025\",\"id\":\"6a102a13-4fac-4a52-be84-c14155d92e05\"},{\"content\":\"Dubois et al., 2024\",\"id\":\"01d27cb9-1f34-4a73-b3bf-fac1f54cf49e\"},{\"content\":\"Jarnow, 2017\",\"id\":\"21e0a365-4f3e-4b3a-a245-e6c249bba61d\"},{\"content\":\"Frey &amp; Llanos-Paredes, 2025\",\"id\":\"336865ee-1683-451b-a785-1ac7bb9134c7\"},{\"content\":\"Caswell &amp; Liang, 2020\",\"id\":\"ef6fd1d3-d1cf-47be-8869-b52141ec2696\"},{\"content\":\"Chan et al., 2022; Chang et al., 2024\",\"id\":\"65c32dbe-12aa-4017-bd18-ded2abdf5db4\"},{\"content\":\"Court &amp; Elsner, 2024; Kocmi et al., 2024; Kocmi et al. 2025\",\"id\":\"fc15492a-4515-4f56-8403-af9083c04d75\"},{\"content\":\"W3Techs, n.d.\",\"id\":\"d1388023-9e20-4bf9-9ada-a222e545ee02\"},{\"content\":\"Brandom, 2023\",\"id\":\"7e516919-7202-43c7-b1e4-4230e84d8dac\"},{\"content\":\"Orimemi, 2025\",\"id\":\"54d5d914-660f-40c0-ba61-52ee5ee74fec\"},{\"content\":\"Judah, 2025\",\"id\":\"23ae2852-56d5-48b3-bb99-25d1675f7754\"},{\"content\":\"Ananny &amp; Pearce, 2025; Beckett &amp; Yaseen, 2023; Simon &amp; Isaza-Ibarra, 2023; Canavilhas, 2022; Ohumu, 2025; Simon, 2025\",\"id\":\"6734b9e8-3840-4537-b1e2-6ff14d8284a1\"},{\"content\":\"Langer, 2025\",\"id\":\"6f4c4029-6d87-467d-9edc-9147e53fd199\"},{\"content\":\"Canavilhas, 2022\",\"id\":\"cd18678a-1784-4761-ba39-7988ed05ecd2\"},{\"content\":\"Ohumu, 2025. See Vo 2025 for another example.\",\"id\":\"33f2991b-81b7-4b31-82a9-ce590959530c\"},{\"content\":\"Valdez Sanabria &amp; Auyanet, 2025\",\"id\":\"9ed420a3-b60c-47c3-99aa-f7adbdda0ffd\"},{\"content\":\"Munoriyarwa et al., 2023\",\"id\":\"8d065a39-271e-4932-9b7e-e47ff50491f0\"},{\"content\":\"Beckett &amp; Yaseen, 2023; Munoriyarwa et al., 2023; Kahn, 2025\",\"id\":\"52871edc-dcac-49d9-aadd-ebd5c02aa7a1\"},{\"content\":\"Gondwe, 2025\",\"id\":\"2642a422-56a4-4dec-be29-cf8479fc2761\"},{\"content\":\"Tokalac, 2023\",\"id\":\"11aff7b0-73bf-4624-90c2-b33a147fa290\"},{\"content\":\"Shahmerdanova, 2025\",\"id\":\"753cfebc-0969-4ed7-9c34-bda719695d92\"},{\"content\":\"Newman &amp; Cherubini, 2025; Ross Arguedas, 2025\",\"id\":\"5e701cd0-1e7f-49e2-b074-5c77d07f1459\"},{\"content\":\"Ross Arguedas, 2025\",\"id\":\"3509cb94-90b7-4508-9d96-35bcfb49c746\"},{\"content\":\"Ross Arguedas, 2025\",\"id\":\"b920bdde-ccc9-4518-9d5a-bc533114acf1\"},{\"content\":\"Simon et al., 2025\",\"id\":\"6d8ee2ff-3c5b-4e20-ac0b-cf943ecbd9e0\"},{\"content\":\"Breaking Language Barriers with AI: Maximizing Accuracy and Efficiency with Machine Translation Technology, 2023\",\"id\":\"e563f6f6-a7a5-458d-8c24-921d72436b69\"},{\"content\":\"Alonso Jim\u00e9nez &amp; Rosado, 2024\",\"id\":\"00442057-afbf-4350-b456-6d338f1f0403\"},{\"content\":\"Noll et al., 2025\",\"id\":\"36cae93d-2512-48ae-89ac-9a10f7c3c3e3\"},{\"content\":\"Moneus &amp; Sahari, 2024\",\"id\":\"0bd57973-aa4a-4949-a865-c2ca4f3672c0\"},{\"content\":\"Asi et al., 2024\",\"id\":\"a78df0d2-5c1e-461b-90cd-390c07838ad1\"},{\"content\":\"Langer, 2025; Schellmann, 2025\",\"id\":\"c7d859e3-e24d-466d-95cc-d6c3e5604228\"},{\"content\":\"Guo et al., 2025; Kocmi et al., 2024; Kocmi et al. 2025; Levit et al., 2017; Moghe et al. 2025; Wang, 2022\",\"id\":\"3bb91335-2924-4562-a784-9ac17a6dc44e\"},{\"content\":\"Lee, 2024; Novytska et al., 2025; Yan et al., 2024\",\"id\":\"2ff7a095-cf02-485f-bbad-c78473cb1dc3\"},{\"content\":\"Schellmann, 2025; Song, 2020\",\"id\":\"36b560a8-a111-4407-b2fb-d97e0823b295\"},{\"content\":\"Leiter et al., 2024\",\"id\":\"1bdee25e-0b19-43d4-b3b5-4996c7a5578c\"},{\"content\":\"Song, 2020\",\"id\":\"4a50ba01-f94e-4c58-bc87-cf03c7de245f\"},{\"content\":\"Song, 2020\",\"id\":\"9fb2ca8d-6954-4aef-9071-c4f5bc0f5888\"},{\"content\":\"Lee, 2024\",\"id\":\"ceadce0d-3baa-423b-a20f-6cf23c2b7865\"},{\"content\":\"Lee, 2024\",\"id\":\"40a6366e-a677-4933-b2fa-32e66d2547b8\"},{\"content\":\"Savoldi et al., 2025; Ullmann, 2022\",\"id\":\"44a65367-ff6a-48cc-8d16-1fcf29e8f84c\"},{\"content\":\"Ghosh &amp; Caliskan, 2023; Prates et al., 2020\",\"id\":\"84ea23e6-e0bd-485a-810c-abdb7f91b264\"},{\"content\":\"Savoldi et al., 2025\",\"id\":\"f4e42e3f-1af3-48de-9895-269a208de86f\"},{\"content\":\"Koenecke et al., 2024; Schellmann, 2025\",\"id\":\"456ac0e4-fb08-4b5f-9af0-cbcf36e9df2f\"},{\"content\":\"Chan et al., 2022\",\"id\":\"0ba9bcba-c84b-476f-9f2f-a043f34b1921\"},{\"content\":\"Chang et al., 2024\",\"id\":\"006f8f2c-ac56-4bef-a8df-f8153ae9e1a8\"},{\"content\":\"Ojewale et al., 2025; Ojo et al., 2025; Pava et al., 2025\",\"id\":\"f7ded5ea-af71-45f2-86db-6af4b98455f8\"},{\"content\":\"Guo et al., 2025\",\"id\":\"0f38f558-9351-4799-a6a5-f11c7322cdc4\"},{\"content\":\"Beckett &amp; Yaseen, 2023\",\"id\":\"4d076988-685c-4446-a782-1496c6d2e415\"},{\"content\":\"Wolfe et al., 2025\",\"id\":\"ed5c0113-f8d5-46c4-b9e7-756182a42d76\"},{\"content\":\"De Coster et al., 2024\",\"id\":\"2ce2c567-0348-482e-99f0-c33ef291dd26\"},{\"content\":\"De Coster et al., 2024\",\"id\":\"e76a0da1-fa58-4520-a90d-0ad8a976746d\"},{\"content\":\"How best to evaluate translation is largely out of scope, but see Leiter 2024.\",\"id\":\"9668eb6e-11d8-4118-8e61-1187028c12a1\"},{\"content\":\"Savoldi et al., 2025\",\"id\":\"bfec6065-c5f7-49e9-acd3-6d662b5cd1ed\"},{\"content\":\"Howcroft &amp; Gkatzia, 2022\",\"id\":\"d4804da8-bf01-4622-a329-082191968ec2\"},{\"content\":\"Qingliang, 2024\",\"id\":\"256d49dd-8fd2-4eee-831e-c03e06b2e6a6\"},{\"content\":\"Hagar et al., 2025\",\"id\":\"75e978b2-5632-46c3-ae10-461e1ff6ff29\"},{\"content\":\"Simon, 2024\",\"id\":\"1c12a305-852f-4372-9dfd-086dacb7cafb\"}]"},"categories":[76],"tags":[21,116,115],"cnti-focus-area":[94],"cnti-topic":[85],"class_list":["post-9315","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-reports","tag-ai","tag-ai-and-journalism","tag-translation","cnti-focus-area-ai-in-journalism","cnti-topic-ai-journalism"],"acf":{"cnti_article_quick_read_link_file":{"simple_value_formatted":"<a href=\"https:\/\/cnti.org\/wp-content\/uploads\/2025\/11\/Transcription-Translation-CNTI-Nov-2025.pdf\" target=\"_blank\">Transcription-Translation-CNTI-Nov-2025<\/a>","value_formatted":{"ID":9316,"id":9316,"url":"https:\/\/cnti.org\/wp-content\/uploads\/2025\/11\/Transcription-Translation-CNTI-Nov-2025.pdf","title":"Transcription-Translation-CNTI-Nov-2025","alt":"","description":"","caption":"","mime_type":"application\/pdf","type":"application","subtype":"pdf","icon":"https:\/\/cnti.org\/wp-includes\/images\/media\/document.png"},"value":"9316","field":{"ID":0,"key":"field_6609f4b7a6240","label":"Quick Read","name":"cnti_article_quick_read_link_file","aria-label":"","prefix":"acf","type":"file","value":null,"menu_order":0,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"group_6609f4b72d6a1","wrapper":{"width":"","class":"","id":""},"return_format":"array","library":"all","min_size":"","max_size":"","mime_types":"","allow_in_bindings":1,"_name":"cnti_article_quick_read_link_file","_valid":1}},"cnti_article_full_report_file":{"simple_value_formatted":"","value_formatted":"","value":"","field":{"ID":0,"key":"field_6609f53ba6241","label":"Full Report","name":"cnti_article_full_report_file","aria-label":"","prefix":"acf","type":"file","value":null,"menu_order":1,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"group_6609f4b72d6a1","wrapper":{"width":"","class":"","id":""},"return_format":"array","library":"all","min_size":"","max_size":"","mime_types":"","_name":"cnti_article_full_report_file","_valid":1}},"cnti_article_parallel_study_sneak_peek_file":{"simple_value_formatted":null,"value_formatted":null,"value":null,"field":{"ID":0,"key":"field_69dce511d7a57","label":"Parallel Study: Sneak Peek","name":"cnti_article_parallel_study_sneak_peek_file","aria-label":"","prefix":"acf","type":"file","value":null,"menu_order":2,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"group_6609f4b72d6a1","wrapper":{"width":"","class":"","id":""},"return_format":"array","library":"all","min_size":"","max_size":"","mime_types":"","allow_in_bindings":1,"_name":"cnti_article_parallel_study_sneak_peek_file","_valid":1}},"post_audio_embed":{"simple_value_formatted":"","value_formatted":"","value":"","field":{"ID":0,"key":"field_68e438d2c1796","label":"Add audio embed iFrame","name":"post_audio_embed","aria-label":"","prefix":"acf","type":"text","value":null,"menu_order":0,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"group_68e438d1a3701","wrapper":{"width":"","class":"","id":""},"default_value":"","maxlength":"","allow_in_bindings":0,"placeholder":"","prepend":"","append":"","_name":"post_audio_embed","_valid":1}},"post_guest_author":{"simple_value_formatted":null,"value_formatted":null,"value":false,"field":{"ID":0,"key":"field_68cb74a05afbe","label":"Add Authors","name":"post_guest_author","aria-label":"","prefix":"acf","type":"repeater","value":null,"menu_order":0,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"group_68cb74a06aa33","wrapper":{"width":"","class":"","id":""},"layout":"table","pagination":0,"min":0,"max":0,"collapsed":"","button_label":"Add Row","rows_per_page":20,"_name":"post_guest_author","_valid":1,"sub_fields":[{"ID":0,"key":"field_68dfff2e26ead","label":"Author's Name","name":"post_guest_authors_author_name","aria-label":"","prefix":"acf","type":"text","value":null,"menu_order":0,"instructions":"","required":0,"id":"","class":"","conditional_logic":0,"parent":"field_68cb74a05afbe","wrapper":{"width":"","class":"","id":""},"default_value":"","maxlength":"","allow_in_bindings":0,"placeholder":"","prepend":"","append":"","parent_repeater":"field_68cb74a05afbe","_name":"post_guest_authors_author_name","_valid":1}]}},"post_summary":{"simple_value_formatted":"<code><em>This data type is not supported! 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