Inequalities, Resistance and Alternatives

African Studies	Education	Media Studies
American Studies	History	Middle East and Islamic Studies
Ancient Near East and Egypt	Human Rights and Humanitarian Law	Musicology
Art History	International Law	Philosophy
Asian Studies	International Relations	Religious Studies
Biblical Studies	Jewish Studies	Slavic and Eurasian Studies
Biology	Languages and Linguistics	Social Sciences
Book History and Cartography	Life Sciences	Theology and World Christianity
Classical Studies	Literature and Cultural Studies

Becoming a Brill Author

Publishing Ethics & AI Policy

Publishing Guides

General Open Access Information

For Authors

For Academic Societies

Metadata: Title Lists, MARC & KBART Files

Catalogs, Flyers and Price Lists

Accessing Brill Products

About Brill & its History

Imprints

Careers

Organization

Corporate Social Responsibility

Help

Part 3 Inequalities, Resistance and Alternatives

In: Technology, Power and Society

Type:: Chapter

DOI:: https://doi.org/10.1163/9789004711396_012

Purchase instant access (PDF download and unlimited online access):

€42.00€35.00 excl. VAT

Add to Cart

Redeem Access Token

Save
Cite
Email this content

Share link with colleague or librarian

You can email a link to this page to a colleague or librarian:
Email this content
or copy the link directly:

https://brill.edhh.ma/abstract/book/9789004711396/b_9876454886568-part3.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Technology, Power and Society

Critical Perspectives on the Global Digital Transformation

Series: Technology, Power & Society, Volume: 1

E-Book ISBN:: 9789004711396

Publisher:: Brill

Print Publication Date:: 21 Jul 2025

Subjects
- Social Sciences

Front Matter

Preliminary Material

Acknowledgements

Introduction

Part 1 Concepts

Chapter 1 Cosmopolitan Critical Data Studies

Chapter 2 Digital and Epistemic Sovereignty in the Science Ecosystem in Latin America

Chapter 3 Experimenting on the Frontier: Imperial Laboratories and Facebook’s Political Effects in Myanmar

Chapter 4 From Datafication to Interpellation: Becoming a Data Subject in Contemporary Surveillance Cultures

Part 2 Digital Cultures and Digital Politics

Chapter 5 ‘BM Girl’ Influencers on Xiaohongshu: Tracing Beauty Discourse, Social Media Challenges, and Consumption Practices in Chinese Society

Chapter 6 Privacy Expectations and Norms: Perceptions of Individual Digital Activists in Turkey’s Xsphere (Twittersphere)

Chapter 7 Detouring, Rerouting, Weaponization: The Secondary Orality of WarTok

Chapter 8 Making Sense of Post-coup Myanmar through Facebook

Chapter 9 Artificial Intelligence Governance Made in China: Negotiating Imaginaries and Power

Part 3 Inequalities, Resistance and Alternatives

Chapter 10 “With great power comes great responsibility”: Lending Visibility to Risky Political Content

Chapter 11 Artificial Intelligence’s Sexual Politics: Three Modes and the Case of Japan

Chapter 12 The Tech Gender Gap: A Closer Look at Women’s Experiences in the Technology Industry

Chapter 13 Beyond the Strictest Computation of the General Proportion

Back Matter

Index

ProCite

RefWorks

Reference Manager

BibTeX

Zotero

EndNote

Abidin, C. (2015). Micro-microcelebrity: Branding babies on the internet. M/C Journal, 18(5).
Abidin, C., & Cover, R. (2018). Gay, famous and working hard on YouTube Influencers, queer microcelebrity publics and discursive activism. In P. Aggleton, R. Cover, D. Leahy, D. Marshall, & M. Lou Rasmussen (Eds.), Youth, Sexuality and Sexual Citizenship (1st ed., pp. 1–23). London: Routledge.
Abidin, C. (2019). Yes Homo: Gay influencers, homonormativity, and queerbaiting on YouTube. Continuum, 33(5), pp. 614–629.
Abidin, C. (2021). Activism in Singapore in the Digital Age: Influencer cultures, meme factories, and networked virality. In P. Natalie, & Z. Shamil (Eds.), Singapore Perspectives: Politics. (pp. 49–55). Institute of Policy Studies.
Abidin, C., (2021a). From “Networked Publics” to “Refracted Publics”: A Companion Framework for Researching “Below the Radar” Studies. Social Media + Society, 7(1).
Ahval, 2018. How social media influenced Turkey’s election campaigns: Looking back. [online] Ahval. Available at: https://ahvalnews.com/turkey-elections/how-social-media-influenced-turkeys-election-campaigns-looking-back
Dogu, B., & Mat, H. (2019). Who sets the agenda? Polarization and issue ownership in Turkey’s political Twittersphere. International Journal of Communication, 13(22), pp. 229–250.
Freedom House (2018). Freedom on the Net – Turkey. Freedom on the Net. Available at: https://freedomhouse.org/report/freedom-net/2018/turkey
Gaddini, K. (2021). ‘Wife, mommy, pastor and friend’: The rise of female Evangelical microcelebrities. Religions, 12(9), p. 758.
Glatt, Z. (2023). The intimacy triple bind: Structural inequalities and relational labour in the influencer industry. European Journal of Cultural Studies, 27(3), pp. 424–440.
Goodwin, A., Joseff, K., & Woolley, S. (2020). Social media influencers and the 2020 U.S. election: Paying ‘regular people’ for digital campaign communication. Austin: University of Texas at Austin Center for Media Engagement.
Hassid, J. (2012). Safety valve or pressure cooker? Blogs in Chinese political life. Journal of Communication, 62(2), 212–230.
Jerslev, A. (2016). Media Times| In The Time of the Microcelebrity: Celebrification and the YouTuber Zoella. International Journal of Communication, 10, pp. 5233–5251.
Kavanaugh, A. L., Sheetz, S. D., Sandoval-Almazan, R., Tedesco, J. C., & Fox, E. A. (2016). Media use during conflicts: Information seeking and political efficacy during the 2012 Mexican elections. Government Information Quarterly, 33(3), pp. 595–602.
Lerner, D. (2018). Turkish opposition gets creative as Erdogan tightens grip on media ahead of elections. Available at: https://www.haaretz.com/middle-east-news/turkey/.premium-erdogan-elections-turkish-opposition-gets-creative-1.6166971
Letsch, C. (2014). Turkey pushes through new raft of ‘draconian’ internet restrictions. The Guardian, 06 February. Available at: https://www.theguardian.com/world/2014/feb/06/turkey-internet-law-censorship-democracy-threat-opposition
Luoma-aho, V., Pirttimäki, T., Maity, D., Munnukka, J., & Reinikainen, H. (2019). Primed authenticity: How priming impacts authenticity perception of social media influencers. International Journal of Strategic Communication, 13(4), pp. 352–365.
Marder, B., Slade, E., Houghton, D., & Archer-Brown, C. (2016). “I like them, but won’t ‘like’ them”: An examination of impression management associated with visible political party affiliation on Facebook. Computers in Human Behavior, 61, pp. 280–287.
Marwick, A. (2015). You May Know Me From YouTube: (Micro)-Celebrity in Social Media. In D. Marshall & S. Redmond (Eds.), A companion to celebrity. Wiley.
Marwick, A., & boyd, d. (2014). Networked privacy: How teenagers negotiate context in social media. New Media & Society, 16(7), pp. 1051–1067.
Marwick, A., & boyd, d. (2011). To see and be seen: Celebrity practice on Twitter. Convergence: The International Journal of Research into New Media Technologies, 17(2), pp. 139–158.
Mercea, D. and Levy, H., (2020). The activist chroniclers of Occupy Gezi: Counterposing visibility to injustice. In: A. McGarry, I. Erhart, H. Eslen-Ziya, O. Jenzen & U. Korkut, (Eds.), The Aesthetics of Global Protest: Visual Culture and Communication, (1st ed., pp. 234–246). Amsterdam University Press.
Mor, Y., Kligler-Vilenchik, N., & Maoz, I. (2015). Political expression on Facebook in a context of conflict: Dilemmas and coping strategies of Jewish-Israeli youth. Social Media + Society, 1(2), pp. 1–10.
Özdemir, S. (2019). Sosyal Medya Ünlüleri̇ Üzeri̇ne Bi̇r İnceleme: Türki̇ye’de Twitter Mi̇kro Ünlüsü Olmak. Moment Journal, 6(2), pp. 406–427.
Pande, R. (2018). It’s just a joke! The payoffs and perils of microcelebrity in India. In C. Abidin & M. Brown (Eds.), Microcelebrity Around the Globe (1st ed., pp. 1–17). Bingley: Emerald Publishing Limited.
Pearce, K. E., & Vitak, J. (2016). Performing honor online: The affordances of social media for surveillance and Impression Management in an honor culture. New Media & Society, 18(11), pp. 2595–2612.
Pearce, K., Vitak, J., & Barta, K. (2018). Socially mediated visibility: Friendship and dissent in authoritarian Azerbaijan. International Journal of Communication, 12, pp. 1310–1331.
Senft, T. (2008). Camgirls: Celebrity and community in the age of social networks (1st ed.). New York: Lang. pp. 1–25.
Tanash, R., Chen, Z., Wallach, D., & Marschall, M. (2017). The decline of social media censorship and the rise of self-censorship after the 2016 failed Turkish coup. In 7th USENIX Workshop on Free and Open Communications on the Internet.
Tang, J. L. (2023). Issue communication network dynamics in connective action: The role of non-political influencers and regular users. Social Media+ Society, 9(2), pp. 1–13.
Tufekci, Z. (2013). ‘Not this one’: Social movements, the attention economy, and microcelebrity networked activism. American Behavioral Scientist, 57(7), pp. 848–870.
Tufekci, Z., & Wilson, C. (2012). Social media and the decision to participate in political protest: Observations from Tahrir Square. Journal of Communication, 62(2), pp. 363–379.
Van Duyn, E., & Collier, J. (2018). Priming and fake news: The effects of elite discourse on evaluations of news media. Mass Communication and Society, 22(1), pp. 29–48.
Wilson, J., & Hahn, A. (2021). Twitter and Turkey: Social media surveillance at the intersection of corporate ethics and international policy. Journal of Information Policy, 11(1), pp. 444–477.
Yanatma, S. (2018). Reuters Institute Digital News Report – Turkey Supplementary Report. Reuters Institute for the Study of Journalism. Available at: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2018-11/Digital%20News%20Report%20-%20Turkey%20Supplement%202018%20FINAL.pdf
Allison, A. (2006). Millennial monsters: Japanese toys and the global imagination. University of California Press.
Adam, A. (1998). Artificial knowing: Gender and the thinking machine. Routledge.
Ashikari, M. (2005). Cultivating Japanese whiteness: The ‘whitening’ cosmetics boom and the Japanese identity. Journal of Material Culture, 10(1), 73–91.
Atanasoski, N., & Vora, K. (2019). Surrogate humanity: Race, robots, and the politics of technological futures. Duke University Press.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity.
Bochev, S. (2021, February 9). Trailing behind: The 2020–2021 Japanese AI landscape. Medium Retrieved 6 July 2023 from https://sbochev.medium.com/trailing-behind-the-2020-2021-japanese-ai-landscape-2568a03b8522
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on Fairness, Accountability, and Transparency, Proceedings of Machine Learning Research, 81, 1–15.
Cadwalladr, C. (2016, December 4). Google, democracy and the truth about internet search. The Guardian. Retrieved from https://www.theguardian.com/technology/2016/dec/04/google-democracy-truth-internet-search-facebook
Chin, C., & Robinson, M. (2020, November 23). How AI bots and voice assistants reinforce gender bias. The Brookings Institution. Report. Retrieved 19 May 2023 from https://www.brookings.edu/research/how-ai-bots-and-voice-assistants-reinforce-gender-bias/
Costa, P., & Ribas, L. (2019). AI becomes her: Discussing gender and artificial intelligence. Technoetic Arts, 17(1–2), 171–193. https://doi.org/10.1386/tear_00014_1
Coursey, K., Pirzchalski, S., McMullen, M., Lindroth, G., & Furuushi, Y. (2019). Living with Harmonay: A personal companion system by Realbotix^TM. In Y. Zhou & M.H. Fisher (Eds.), AI Love you (pp. 77–95). Springer.
Crawford, K. & Calo, R. (2016). There is a blind spot in AI Research. Nature, 538, 311–313.
Danaher, J. (2019). Building better sex robots: Lessons from feminist pornography. In Y. Zhou & M.H. Fisher (Eds.), AI Love you (pp. 133–147). Springer.
Devlin, K. (2018). Turned on: Sciences, sex and robots. Bloomsbury Sigma.
Devlin, K., & Belton, O. (2020). The measure of a woman: Fembots, fact and fiction. In S. Cave, K. Dihal & S. Dillon (Eds.), AI narratives: A history of imaginative thinking about intelligent machines (pp. 357–381). Oxford University Press.
Dirksen, N., & Takahashi, S. (2020, October 5). Artificial intelligence in Japan 2020: Actors, market, opportunities and digital solutions in a newly transformed world. Embassy of the Kingdom of the Netherlands, Innovation, Science and Economic Affairs and Climate Policy. Retrieved 27 June 2023 from https://www.rvo.nl/sites/default/files/2020/12/Artificial-Intelligence-in-Japan-final-IAN.pdf
Dooley, S., Downing, R., Wei, G., Shankar, N., Thymes, B., Thorkelsdottir, G., Kurtz-Miott, T., Mattson, R., Obiwumi, O., Cherepanova, V., Goldblum, M., Dickerson, J.P., Goldstein, T. (2021, October 21). Comparing human and machine bias in face recognition. arXiv preprint arXiv:2110.08396. https://doi.org/10.48550/arXiv.2110.08396
Döring, N. (2017). Vom Internet yum Robotersexä Forschungsstand und Herausforderungen für die Sexualwisenschaft. Zeitschrift für Sex-Forschung, 30(1), 35–57. https://doi.org/10.1055/s-0043-101471
Döring, N., & Pöschl, S. (2018). Sex toys, sex doll, sex robots: Our under-researched bed-fellows. Sexologies, 27(3), e51–e55. https://doi.org/10.1016/j.sexol.2018.05.009
Elias, A., Gill, R., & Scharff, C. (2017). Aesthetic Labour: Beauty Politics in Neoliberalism. In A. Elias, R. Gill, & C. Scharff (Eds.), Aesthetic Labour: Rethinking Beauty Politics in Neoliberalism (pp. 3–50). Palgrave Macmillan.
Eyssel, F., & Hegel, F. (2012). (S)he’s got the look: Gender stereotyping of robots. Journal of Applied Social Psychology, 42(9), 2213–2230. https://doi.org/10.1111/j.1559-1816.2012.00937.x
Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2020). Gender bias in chatbot design. In A. Følstad et al. (Eds.), Chatbot research and design. Paper presented at CONVERSATIONS 2019 (Lecture notes in computer science, vol. 11970). Springer, 79–93. https://doi.org/10.1007/978-3-030-39540-7_6
Frummer, Y. (2020). The short, strange life of the first friendly robot. IEEE Spectrum. 21 May 1010. Retrieved 16 November 2023 from https://spectrum.ieee.org/the-short-strange-life-of-the-first-friendly-robot
Galbraith, P. W. (2014). The moe manifesto: An insider’s look at the worlds of manga, anime, and gaming. Tuttle.
Gaskell, A. (2022, 6 September). How biased Google Search results affect hiring decisions. Forbes (online). Retrieved 9 May 2023 from https://www.forbes.com/sites/adigaskell/2022/09/06/how-biased-google-search-results-affect-hiring-decisions/
Gehl, R.W., Moyer-Horner, L. & Yeo, S.K. (2017). Training computers to see internet pornography: Gender and sexual discrimination in computer vision science. Television & New Media, 18(6): 529–547.
Goodfellow, I. J., Pouget-Abadie, J., Mizra, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Genjio, Y. (2014). Generative adversarial networks. NeurIPS Processing: Advances in Neural Information Processing Systems 27 (NIPS 2014). Retrieved 18 May 2023 from https://doi.org/10.48550/arXiv.1406.2661
Government of Japan (2015). 日本再興戦略改訂 2015 [Japan Revitalisation Strategy. Revised in 2015].
Government of Japan (2016). 科学技術基本計画 [The Fifth Science and Technology Basic Plan].
Government of Japan (2021). 科学技術・イノベーション基本計画 [The Sixth Science, Technology and Innovation Basic Plan].
Greimel, H. (2019, August 8). Automotive sector tops r&d spending in Japan. Automotive News Europe. Retrieved 27 June 2023 from https://europe.autonews.com/automakers/automotive-sector-tops-rd-spending-japan
Hao, K. (2020, October 20). A deepfake bot is being used to “undress” underage girls. MIT Technology Review. Retrieved 31 May 2023 from https://www.technologyreview.com/2020/10/20/1010789/ai-deepfake-bot-undresses-women-and-underage-girls/
Henne, K., Shelby, R., & Harb, J. (2021). The datafication of #MeToo: Whiteness, racial capitalism, and anti-violence technologies. Big Data & Society, 8(2). Retrieved 18 May 2023 from https://journals.sagepub.com/doi/10.1177/20539517211055898
Henry, N., & Powell, A. ([2016]2018). Technology-facilitated sexual violence: A literature review of empirical research. Trauma, Violence, & Abuse, 19(2): 195–208.
Hunt, E. (2016, March 24). Tay, Microsoft’s AI chatbot, gets a crash course in racism from Twitter. The Guardian. Retrieved 18 May 2023 from https://www.theguardian.com/technology/2016/mar/24/tay-microsofts-ai-chatbot-gets-a-crash-course-in-racism-from-twitter
Japanese Society for Artificial Intelligence, The (2020). 人工知能 [Artificial Intelligence]. Vol. 35, No. 5.
Jensen, C. B., & Blok, A. (2013). Techno-animism in Japan: Shinto Cosmograms, actor-network theory and the enabling powers of non-human agencies. Theory, Culture & Society, 30(2), 84–115. https://doi.org/10.1177/0263276412456564
Kakizaki, M., & Suzuki, T. (2020, October 2). フェイクポルノ : フェイクポルノ初摘発 AIで作成、公開 2容疑者逮捕 [Fakeporunography, uncovered for the first time. Two suspects arrested for creating it with AI and releaesing it online]. The Mainichi Newspaper, Tokyo Evening Edition, 7.
Kaufman, E. M. (2022). Reprogramming consent: Implications of sexual relationships with artificially intelligent partners. In E. M. Kaufman, Nuances of sexuacl consent. Routledge. https://doi.org/10.4324/9781003276180
Katsuno, H., & White, D. (2022). Haptic creatures: Tactile affect and human-robot intimacy in Japan. In Y. Minowa & R. Belk (Eds.), Consumer culture theory in Asia: History and contemporary issues (pp. 242–262). Routledge.
Kayser-Bril, N. (2020, September 11). Female historians and male nurses do not exist, Google Translate tells its European users. Algorithm Watch. Retrieved 2 August 2021 from https://algorithmwatch.org/en/google-translate-gender-bias/
Kokumai, A., & Kuribayashi, F. (2020, March 19). 高輪新駅の AI「さくらさん」、セクハラ受け流しで物議 [AI Sakura-san at a new station in Takanawa, dodging sexual harassment brings about a controversy]. The Asahi Shimbun Digital. Retrieved 3 July 2013 from https://www.asahi.com/articles/ASN3M6RPHN3MUTIL044.html
Kragen, P. (2017, September 22). The world’s first talking sex robot, Harmony, is ready to make her debut. Australian Financial Review. Retrieved 21 June 2023 from https://www.afr.com/technology/the-worlds-first-talking-sex-robot-harmony-is-ready-to-make-her-debut-20170922-gymism
Kuwabara, T. (2023, November 12). AI 生成の児童性的画像、サイト運営者「削除するつもりない」… 専門家から対策求める声 [AI generated child pornographic images, website owner ‘we’re not going to delete them’ … experts ask for counter-measures]. Yomiuri Shinbun Online. Retrieved 25 November 2023 from https://www.yomiuri.co.jp/national/20231112-OYT1T50002/
Kuczmarski, J. (2018, December 6). Reducing gender bias in Google Translate. Retrieved 26 May 2023 from https://blog.google/products/translate/reducing-gender-bias-google-translate/
Latour, B. (2005). Reassembling the social: An introduction to Actor-Network-Theory. Oxford University Press.
Levy, D. (2007). Love and sex with robots: The evolution of human-robot relationships. HarperCollins.
Luka, Inc. (2024). Replika. Retrieved August 16, 2024 from https://replika.com
Matsuo, Y. (2020). 人工知能関連技術の歴史と技術動向 [History and trend of artificial intelligence technologies]. Denki Joho Tsushin Gakkaishi [Journal of IEICE], 103(5), 450–455.
McDonnell, M., & Baxter, D. (2019). Chatbots and gender stereotyping. Interacting with Computers, 31(2), 116–121.
Munn, L. (2022). The uselessness of AI ethics. AI and Ethics, 3, 869–877. https://doi.org/10.1007/s43681-022-00209-w
Natale, S. (2019). If software is narrative: Joseph Weizenbaum, artificial intelligence and the biographies of ELIZA. New Media & Society, 21(3), 712–728. https://doi.org/10.1177/14614448188049
Neff, G., & Nagy, P. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915–4931
Nguyen, D., & Hekman, E. (2024). The news framing of artificial intelligence: a critical exploration of how media discourses make sense of automation. AI and Society, 39(2), 437–451. https://doi.org/10.1007/s00146-022-01511-1
NHK Science & Technology Research Laboratories (2018, May). AI キャスターがニュースチェック11 に登場 [AI newscaster appeared in News Check 11]. Giken Dayori [Newsletter of Science & Technology Research Lab]. Retrieved 23 November 2023 from https://www.nhk.or.jp/strl/publica/giken_dayori/158/3.html
Noble, S. U. (2012). Missed connections: What search engines say about women. Bitch, 12(54), 37–41. Retrieved 30 May 2023 from https://safiyaunoble.files.wordpress.com/2012/03/54_search_engines.pdf
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
Nyholm, S., & Frank, L. (2019). It loves me, it loves me not: Is it morally problematic to design sex robots that appear to “love” their owners? Techné, 23(3), 402–424. https://doi.org/10.5840/techne2019122110
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishers.
Osaka, N. (2020, May 25). 駅の案内で大活躍⌈AI さくらさんの別の顔社員向けヘルプデスクなど導入企業は300社 [AI Sakura-san, popular at a train station’s support desks, also introduced for employees’ helpdesk in 300 companies]. Toyo Keizai Online. Retrieved 7 July 2023 from https://toyokeizai.net/articles/-/352077?page=2
Perez, S. (2022, February 24). Siri gains a new gender-neutral voice option in latest iOS update. TechCrunch. Retrieved 16 June 2023 from https://techcrunch.com/2022/02/24/siri-gains-a-new-gender-neutral-voice-option-in-latest-ios-update/?guccounter=1
Petman, D. (2019). Love in the time of Tamagotchi. Theory, Culture & Society, 26(2–3), 189–208. https://doi.org/10.1177/0263276409103117.
Phan, T. (2017). The materiality of the digital and the gendered voice of Siri. Transformations, 29(11), p. 1–24. Retrieved 30 May 2023 from https://www.semanticscholar.org/paper/The-Materiality-of-the-Digital-and-the-Gendered-of-Phan/f1b11ccf3e30632b65e6b781dbf2d0e3013568c7
Reeves, B., & Nass, C. I. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.
Robertson, J. (2017) Robo sapiens japonicus: Robots, gender, family, and the Japanese nation. University of California Press.
Roquet, P. (2022). The immersive enclosure: Virtual reality in Japan. Columbia University Press.
Schick, N. (2020). Deepfakes: The coming Infocalypse. Twelve.
Shadel, J. D. (2022, April 7). How Google’s autocomplete predictions encouraged transphobic searches. Them. Retrieved 23 May 2023 from https://www.them.us/story/google-autocomplete-suggestions-transphobia-celebrities
Sheuerman, M. K., Paul, J. M., & Brubaker, J. R. (2019). How computers see gender: an evaluation of gender classification in commercial facial analysis and image labelling services. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW, Article 144).
Shibata, H., & Watanabe, A. (2023, May 27). AI 政策、推進色強く戦略会議提言、リスクは列挙 [AI policy, its strategic council promotes AI, with its risks mentioned]. Asahi Shimbun. Morning Edition (p. 3).
Shino, K., Yamazaki, K., & Niitsuma, T. (2023, November 21). チャットGPT 職業に性別偏見 [ChatGPT, gender biases in occupation]. Asashi Shmbun. Tokyo, Morning Edition (p. 1).
Sodhi, G. S., & Kaur, J. (2022). Scientific racism faced by Indian fingerprint scientists during colonial rule: Need to correct a historical wrong. Journal of Scientific Temper, 10(1&2), 52–69.
Solomon, O., & Levi, S. (2016, December 16). How Google’s search algorithm spreads false information with a rightwing bias. The Guardian. Retrieved 26 May 2023 from https://www.theguardian.com/technology/2016/dec/16/google-autocomplete-rightwing-bias-algorithm-political-propaganda
Sony (n.d.) AI アナウンサー人工知能でより人に近い音声読み上げを実現 [Achieving more human-like voice reading using artificial intelligence]. Retrieved 23 November 2023 from https://www.sony.jp/professional/ai-announcer/
Steinberg, M. (2019). The platform economy: How Japan transformed the consumer internet. University of Minnesota Press.
Sterri, A. B., & Earp, B. D. (2021, November 10). The ethics of sex robots. In Carissa Véliz (ed.), The Oxford handbook of digital ethics (online edition, Oxford Academic). https://doi.org/10.1093/oxfordhb/9780198857815.013.13
Strengers Y. & Kennedy, J. (2020). The smart wife: Why Siri, Alexa, and other smart home devices need a feminist reboot. MIT Press.
Tanaka, H., & Ho, M. H. S. (2022). Romancing AI? Gender and new digital intimacies in contemporary Japan. Paper presented at the Conference “AI and the human: Cross-cultural perspectives on science and fiction.” Humboldt Institute for Internet and Society, The Japanese-German Center Berlin, 11–13 May 2022.
Taylor, E. (2007). Dating-simulation games: Leisure and gaming of Japanese youth culture. Southeast Review of Asian Studies, 29, 192–208.
Tohata, K. (2023, June 22). AI に心の相談弱さが生む人間の役割 [Mental health counseling with AI. Humans’ role emerging from their weakness]. Asahi Shimbun. Morning Edition (p. 13).
Tomita, H. (2005). Keitai and the intimate stranger. In Ito, M., Okabe, D., & Matsuda, M. (Eds.), Personal, portable, pedestrian: Mobile phones in Japanese life (pp. 183–204). MIT Press.
Tuchman, G. (1978). Introduction: The symbolic annihilation of women by the mass media. In Tuchman, G., Daniels, A.K., & Benét, J. (Eds.), Hearth and home: Images of women in the mass media (pp. 3–38). Oxford University Press.
Urbi, J. (2018, August 8). Some transgender drivers are being kicked off Uber’s app. CNBC. Retrieved 28 August 2021 from https://www.cnbc.com/2018/08/08/transgender-uber-driver-suspended-tech-oversight-facial-recognition.html
Verma, P. (2023, November 5). AI fake nudes are booming. It’s ruining real teens’ lives. The Washington Post. Retrieved 22 November 2023 from https://www.washingtonpost.com/technology/2023/11/05/ai-deepfake-porn-teens-women-impact/
Vlasceanu, M., & Amodio, D. M. (2022, July 12). Propagation of societal gender inequality by internet search algorithms. PNAS, 119(29) e2204529119. https://doi.org/10.1073/pnas.2204529119
Vorsino, Z. (2021). Chatbots, gender, and race on Web2.0 platforms: Tay.AI as monstrous femininity and abject whiteness. Signs: Journal of Women in Culture and Society, 47(1), 105–127.
Watanabe, R. (2022, June 15). 最高裁判所、AI アバター接客「AI さくらさん」を導入 [AI avater receptionist, AI Sakura-san introduced at the Supreme Court]. Digital Gyosei/Digital Administration. Retrieved 3 July 2023 from https://www.digital-gyosei.com/post/2022-06-15-news-supremecourt-ai/
West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. New York: AI Now Institute. Retrieved 5 April 2019 from https://ainowinstitute.org/discriminatingsystems.html.
Yomiuri Shimbun, The (2023, June 7). AI が生み出した女性モデルのグラビア写真集、集英社が販売中止 … 著作権を問題視する声 [AI female model’s gurabia photobook withdrawn by Shueisha. Public opinions question its copyright]. Yomiuri Shimbun. Retrieved from 3 July 2023 from https://www.yomiuri.co.jp/culture/20230607-OYT1T50185/
Zou, J., & Schiebinger, L. (2018). Design AI so that it’s fair. Nature, 559 (12 July 2018), 324–326.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
Amon, M. J. (2017). Looking through the Glass Ceiling: A Qualitative Study of STEM Women’s Career Narratives. Frontiers in Psychology, 8, 1–10.
- Crossref
- Export Citation
Banaji, M. R., & Greenwald, A. G. (2016). Blindspot: Hidden biases of good people. Bantam.
Baron, A. S., & Banaji, M. R. (2006). The development of implicit attitudes: Evidence of race evaluations from ages 6 and 10 and adulthood. Psychological Science, 17(1), 53–58. https://doi.org/10.1111/j.1467-9280.2005.0166
Beede, D. N., Julian, T. A., Langdon, D., McKittrick, G., Khan, B., & Doms, M. E. (2011). Women in STEM: A Gender Gap to Innovation. SSRN Electronic Journal.
- Crossref
- Export Citation
Besecke, L. M., & Reilly, A. H. (2017). Factors influencing career choice for women in science, mathematics, and Technology: the importance of a transforming experience. Advancing Women in Leadership Journal, 21.
- Crossref
- Export Citation
Boysen, G. A., & Vogel, D. L. (2008). The relationship between level of training, implicit bias, and multicultural competency among counselor trainees. Training and Education in Professional Psychology, 2(2), 103–110. https://doi.org/10.1037/1931-3918.2.2.103
Cauterucci, C. (2017, February 21). The sexism described in Uber employee’s report is why women leave Tech – Or don’t enter at all. Slate Magazine. https://slate.com/human-interest/2017/02/the-sexism-in-uber-employees-report-is-why-women-leave-tech-or-dont-enter-at-all.html
CBS. Centraal Bureau voor de Statistiek (2023). Al 23 jaar op rij meer vrouwen dan mannen in hoger onderwijs. Centraal Bureau voor de Statistiek. https://www.cbs.nl/nl-nl/nieuws/2023/10/al-23-jaar-op-rij-meer-vrouwen-dan-mannen-in-hoger-onderwijs
Centraal Bureau voor de Statistiek (2024). Beroepen van werkenden. https://www.cbs.nl/nl-nl/visualisaties/dashboard-arbeidsmarkt/werkenden/beroepen-van-werkenden
Chadwick, A. J., & Baruah, R. (2019). Gender disparity and implicit gender bias amongst doctors in intensive care medicine: A ‘disease’ we need to recognise and treat. Journal of the Intensive Care Society, 21(1), 12–17.
- Crossref
- Export Citation
Dixon-Fyle, S., Dolan, K., Hunt, D. V., & Prince, S. (2020). Diversity wins: How inclusion matters. In McKinsey & Company. https://www.mckinsey.com/featured-insights/diversity-and-inclusion/diversity-wins-how-inclusion-matters
Duhaime-Ross, A. (2014, September 25). Apple promised an expansive health app, so why can’t I track menstruation? The Verge. https://www.theverge.com/2014/9/25/6844021/apple-promised-an-expansive-health-app-so-why-cant-i-track
Edgar, G. (2021). 5 actions tech employers can take to retain women. Women in Technology. https://www.womenintech.co.uk/5-actions-tech-employers-can-take-to-retain-women
Ellemers, N. (2014). Women at work. Policy Insights From the Behavioral and Brain Sciences, 1(1), 46–54.
- Crossref
- Export Citation
FME Actieagenda ‘Vrouwen in Techniek – Op weg naar 30% in 2030ʹ | FME. (n.d.). FME. https://www.fme.nl/fme-actieagenda-vrouwen-techniek-op-weg-naar-30-2030#:~:text=Het%20aandeel%20vrouwen%20in%20de,om%20deze%20ambitie%20te%20behalen.
Friedmann, E., & Efrat-Treister, D. (2022). Gender Bias in STEM hiring: Implicit In-Group gender favoritism among men managers. Gender & Society, 37(1), 32–64.
- Crossref
- Export Citation
Global Gender Gap Report. (2023). World Economic Forum. https://www.weforum.org/reports/global-gender-gap-report-2023/
Idris, R., Govindasamy, P., Nachiappan, S., & Bacotang, J. (2023). Examining moderator factors influencing students’ interest in STEM careers: the role of demographic, family, and gender. International Journal of Academic Research in Progressive Education and Development, 12(2). https://doi.org/10.6007/ijarped/v12-i2/17609
Kenny, E. J., & Donnelly, R. (2019). Navigating the gender structure in information technology: How does this affect the experiences and behaviours of women? Human Relations, 73(3), 326–350.
- Crossref
- Export Citation
Kitzinger, J., University, C., Chimba, M., Williams, A., Haran, J., Boyce, T., Cardiff School of Journalism, Media and Cultural Studies Cardiff University, & UK Resource Centre for Women in Science, Engineering and Technology (UKRC). (2008). Gender, stereotypes and expertise in the press: How Newspapers Represent Female and Male Scientists. Gwasg y Bwthyn. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=39c4660cc9bb56278e881cad1c4e98b30558afa4
Kuckartz, U. (2014). Qualitative Text analysis: A Guide to Methods, Practice and Using Software (1st ed.). SAGE Publications Limited. https://doi.org/10.1007/978-3-030-15636-7_8
Makarem, Y., & Wang, J. (2019). Career experiences of women in science, technology, engineering, and mathematics fields: A systematic literature review. Human Resource Development Quarterly, 31(1), 91–111.
- Crossref
- Export Citation
Microsoft Diversity & Inclusion Report (2023). Microsoft. https://www.microsoft.com/en-us/diversity/inside-microsoft/annual-report?activetab=innovation-spotlights%3aprimaryr4
Ministry of General Affairs (2022). Ministerie van Algemene Zaken. Emancipatie een opdracht voor ons allemaal. Nieuwsbericht | Rijksoverheid.nl. https://www.rijksoverheid.nl/actueel/nieuws/2022/11/18/emancipatie-een-opdracht-voor-ons-allemaal
Moser, C. E., & Branscombe, N. R. (2021). Male Allies at Work: Gender-Equality Supportive Men Reduce Negative Underrepresentation Effects among women. Social Psychological and Personality Science, 13(2), 372–381.
- Crossref
- Export Citation
Nguyen, D., & Beijnon, B. (2023). The data subject and the myth of the ‘black box’ data communication and critical data literacy as a resistant practice to platform exploitation. Information Communication & Society, 27(2), 1–17.
- Crossref
- Export Citation
NOS (2023). Deeltijdwerk al snel na diploma in trek onder vrouwen, ‘niet per se slecht’. NOS. https://nos.nl/artikel/2471090-deeltijdwerk-al-snel-na-diploma-in-trek-onder-vrouwen-niet-per-se-slecht
Papastergiou, M. (2008). Are Computer Science and Information Technology still masculine fields? High school students’ perceptions and career choices. Computers & Education, 51(2), 594–608.
- Crossref
- Export Citation
Perez, C. C. (2019). Invisible women: Data bias in a world designed for men. Abrams Press.
Sassler, S., Glass, J., Levitte, Y., & Michelmore, K. M. (2017). The missing women in STEM? Assessing gender differentials in the factors associated with transition to first jobs. Social Science Research, 63, 192–208.
- Crossref
- Export Citation
Schinkels, C. (2021). De IT girl: Hoe overleef je een door mannen gedomineerde werkvloer? Culemborg: van Duuren Management.
Schorr, A. (2019). Pipped at the post: Knowledge gaps and expected low parental IT competence ratings affect young women’s awakening interest in professional careers in information science. Frontiers in Psychology, 10, 1–18.
- Crossref
- Export Citation
Statista (2023). Google: Global Corporate Demography 2023, by gender and department. https://www.statista.com/statistics/311805/google-employee-gender-department-global/
Tandrayen-Ragoobur, V., & Gokulsing, D. (2021). Gender gap in STEM education and career choices: what matters? Journal of Applied Research in Higher Education, 14(3), 1021–1040. https://doi.org/10.1108/jarhe-09-2019-0235
VHTO (2022).Vrouwen in bèta, techniek en IT Hoe behoud je ze als organisatie? VHTO. 1–27. https://www.vhto.nl/wp-content/uploads/2022/09/Whitepaper_Vrouwen_in_beta__techniek_en_IT__hoe_behoud_je_ze_als_organisatie.pdf
De Vleeschouwer, E., Wiemers, S., Zandvliet, K., & SEOR BV. (2020). Kiezen voor technisch vmbo: de rol van ouders en hun beeld van techniek. In SEOR. https://www.seor.nl/Cms_Media/S1311KiezenvoortechnischvmboDerolvanoudersenhunbeeld-van-techniek.pdf
Wang, M., & Degol, J. L. (2016). Gender gap in Science, Technology, Engineering, and Mathematics (STEM): current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. https://doi.org/10.1007/s10648-015-9355-x
Yi Wang and David Redmiles 2019. Implicit gender biases in professional software development: an empirical study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS ‘19). IEEE Press, 1–10.
- Crossref
- Export Citation
Wentling, R., & Thomas, S. (2009). Workplace culture that hinders and assists the career development of women in information technology. Information Technology, Learning & Performance Journal, 25(1). 25–42.
De Wit, J., & Kalkhoven, F. (2019). Tekorten ict-beroepen nemen verder toe. https://www.uwv.nl/imagesdxa/factsheet-ict-beroepen_tcm94-447183.pdf
Wright, T. (2015). Women’s experience of Workplace Interactions in Male‐Dominated Work: the intersections of gender, sexuality and occupational group. Gender Work and Organization, 23(3), 348–362.
- Crossref
- Export Citation
Yerkes, Mara A., and Belinda Hewitt, ’Part-time strategies of women and men of childbearing age in the Netherlands and Australia’, in Heidi Nicolaisen, Hanne Kavli, and Ragnhild Steen Jensen (eds), Dualisation of Part-Time Work: The Development of Labour Market Insiders and Outsiders (Bristol, 2019; online edn, Policy Press Scholarship Online, 23 Jan. 2020), https://doi.org/10.1332/policypress/9781447348603.003.0011
Zacharia, Z. C., Hovardas, T., Xenofontos, N., Pavlou, I., & Irakleous, M. (2020). Education and employment of women in science, technology and the digital economy, including AI and its influence on gender equality. In European Parliament, European Parliament (Study PE 651.042). https://www.europarl.europa.eu/RegData/etudes/STUD/2020/651042/IPOL_STU(2020)651042_EN.pdf
Zarrett, N. R., & Malanchuk, O. (2005). Who’s computing? Gender and race differences in young adults’ decisions to pursue an information technology career. New Directions for Child and Adolescent Development, 2005(110), 65–84.
- Crossref
- Export Citation
Zollman, A. (2012). Learning for STEM literacy: STEM Literacy for Learning. School Science and Mathematics, 112(1), 12–19. https://doi.org/10.1111/j.1949-8594.2012.00101.x
Arthern, P. J. (1981). Aids unlimited: the scope for machine aids in a large organization. Aslib Proceedings, 33(7), 309–319.
Barnes, L. W. (1972). The language of incentive in Jonathan Swift’s Gullivers Travels. Morehead State University.
Benkova, L., Munkova, D., Benko, L., & Munk, M. (2021). Evaluation of English–Slovak neural and statistical machine translation. Applied Sciences, 11(7), 2948.
Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: A case study. In J. Su, K. Duh, & X. Carreras (Eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 257–267). Association for Computational Linguistics. Austin, Texas, USA. https://doi.org/10.18653/v1/D16-1025.
Besacier, L. & Schwartz, L. (2015). Automated translation of a literary work: a pilot study. Proceedings of NAACL-HLT Fourth Workshop on Computational Linguistics for Literature [Denver, Colorado, June 4, 2015], 114–122.
Blommaert, J. (2010). The sociolinguistics of globalisation. Cambridge: Cambridge University Press.
Bonner, A. (1994). Doctor illuminatus: A Ramon Llull reader. Princeton: Princeton University Press.
Booth, A. D. & Booth, K. H. V. (1956). Automatic digital calculators (2nd ed.). London: Butterworths Scientific Publications.
Broomans, P., F. Duan, & Hedberg, A. (2022). Views on World Literature: Cultural transfer and translation in the context of an Online International Exchange (OIE) project – A case study of China, the Netherlands and Sweden. In T. Clark & P. Gordon (Eds.), Beyond Babel. Scholarly organizations and the study of languages and literatures (pp. 77–95). Amsterdam/Philadelphia: John Benjamins Publishing Company.
Brown, P. F., J. Cocke, S. A. Della Pietra, V. J. Della Pietra, F. Jelinek, J. D. Lafferty, R.L. Mercer, & Roossin, P. S. (2003). A Statistical Approach to Machine Translation. In S. Nirenbrug, H. L. Somers & Y. Wilks (Eds.), Reading in Machine Translation, pp. 355–362. Cambridge/London: MIT Press.
Castilho, S., & Resende, N. (2022). Post-editese in literary translations. Information, 13(2), 66.
Christensson, P. (2011, March 12). Boolean Definition. TechTerms, https://techterms.com/definition/boolean.
Collet, B. (2015). From intermarriage to conjugal mixedness: Theoretical considerations illustrated by empirical data in France. The ANNALS of the American Academy of Political and Social Science, 662(1), 129–147.
Comen, E. (2018). Check out how much a computer cost the year you were born. USA Today, https://eu.usatoday.com/story/tech/2018/06/22/cost-of-a-computer-the-year-you-were-born/36156373/.
Daylight, E. G. (2015). Towards a historical notion of Turing – the father of computer science. History and Philosophy of Logic, 36(3), 205–228.
Declercq, C. (2012). Advertising and localization. In K. Malmkjær & K. Windle (Eds.), The Oxford Handbook of Translation Studies, pp. 262–272. Oxford: Oxford University Press.
Dewey, M. (2010). English as a lingua franca and globalization: An interconnected perspective. International Journal of Applied Linguistics, 17(3), 332–354.
Ehrensberger-Dow, M., & Heeb, A. H. (2016). Investigating the ergonomics of a technologized translation workplace. In R. Muñoz Martín (Ed.), Reembedding translation process research, pp. 69–88. Amsterdam: John Benjamins.
Elias, A., & Mansouri, F. (2020). A systematic review of studies on interculturalism and intercultural dialogue. Journal of Intercultural Studies, 41(4), 490–523.
Even-Zohar, I. (1979). Polysystem theory. Poetics Today, 1(1/2), 287–310.
Federici, F. M., Declercq, C., Cintas, J. D., & Piñero, R. B. (2023). Ethics, automated processes, machine translation, and crises. In H. Moniz & C. Parra Escartín (Eds.). Towards responsible machine translation: Ethical and legal considerations in machine translation (pp. 135–156). Cham: Springer International Publishing.
Fitria, T. N. (2021). Gender bias in translation using google translate: Problems and solution. Language Circle: Journal of Language and Literature, 15(2), 285–292.
Franssen, T. (2015). Diversity in the large-scale pole of literary production: An analysis of publishers’ lists and the Dutch literary space, 2000–2009. Cultural Sociology, 9(3), 382–400.
Garcia, I. (2014). Computer-aided translation. In Chan Sin-wai (Ed.), The Routledge Encyclopedia of Translation Technology, pp. 68–87. Abingdon: Routledge.
Guerberof-Arenas, A. & Toral, A. (2020). The impact of post-editing and machine translation on creativity and reading experience. Translation Spaces, 9(2), 255–282.
Guerberof-Arenas, A. & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Translation Spaces 11(2), 184–212.
Haigh, T. & Priestley, M. (2016). Historical reflections: Where code comes from: architectures of automatic control from Babbage to Algol. Communications of the ACM, 59(1), 39–44.
Hartley, A., & Paris, C. (1997). Multilingual document production from support for translating to support for authoring. Machine Translation, 12(1–2), 109–129.
Hassan, H., Aue, A., Chen, C., Chowdhary, V., Clark, J., Federmann, C., Huang, X., Junczys-Dowmunt, M., Lewis, W., Li, M., & Liu, S. (2018). Achieving human parity on automatic chinese to english news translation. arXiv preprint arXiv:1803.05567.
Heilbron, J., Sapiro, G. (2007). Outline for a sociology of translation: Current issues and future prospects. In M. Wolf & A. Fukari (Eds.), Constructing a Sociology of Translation, pp. 93–107. Amsterdam: John Benjamin Publishing.
Hirschberg, J. & Manning, C. D. (2015). Advances in natural language processing. Science 349(6245), 261–266.
Hoover, C. & Sommer, H. (2010). Automated translation of Chinese-to-English creative literature. Minds@UW, University of Wisconsin, Student Research Day. https://minds.wisconsin.edu/handle/1793/47395
House, J. (2003). English as a lingua franca: A threat to multilingualism? Journal of Sociolinguistics, 7(4), 556–578.
Hu, K. & Li, X. (2023). The creativity and limitations of AI neural machine translation. A corpus-based study of DeepL’s English-to-Chinese translation of Shakespeare’s plays. Babel 69(4), 546–563.
Hutchins, J. (1994). Machine translation: History and general principles. In R.E. Asher (Ed.), The Encyclopaedia of Languages and Linguistics (vol. 5), pp. 2322–2332. Oxford: Pergamon Press.
Hutchins, J. (1997). From first conception to first demonstration: The nascent years of machine translation, 1947–1954. a chronology. Machine Translation, 12, 195–252.
Hutchins, J. (1998). The origins of the translators workstation. Machine Translation, 13, 287–307.
Hutchins, J. (1999). From the archives … Warren Weaver Memorandum, July 1949. MT News International, 22, 5–6.
Hutchins, J. (2001). Machine translation and human translation: In competition or in complementation? International Journal of Translation, 13(1–2), 1–20.
Hutchins, J. (2013). ALPAC: The (In)Famous Report. In S. Nirenbrug, H.L. Somers & Y. Wilks (Eds.), Reading in Machine Translation, pp. 131–136. Cambridge/London: MIT Press.
Hutchins, J. (2014). The history of machine translation in a nutshell. Technical Report. University of East Anglia.
Kaibao, H. & Xiaoqian, L. (2023). The creativity and limitations of AI neural machine translation. Babel, 69(4), 546–563.
Kenny, D., & Winters, M. (2020). Machine translation, ethics and the literary translator’s voice. Translation Spaces, 9(1), 123–149.
Kloepfer, R. & Shaw, P. (1981). Intra- and Intercultural Translation. Poetics Today 2(4), 29–37.
Kofi Adarkwah, G. (2022). Why “Think Globally, Act Locally” is a dangerous strategy for emerging markets. California Management Review. Online, https://cmr.berkeley.edu/2022/07/why-think-globally-act-locally-is-a-dangerous-strategy-for-emerging-markets/
Kotze, H. (2020). Translation, contact linguistics and cognition. In F. Alves & A. Jakobsen (Eds.) The Routledge Handbook of Translation and Cognition, pp. 113–132. Abingdon: Routledge.
Kotzsch, R. (2021). 5 CEO Insights for the Localization Industry in 25 Years. LinkedIn post, https://www.linkedin.com/pulse/7-ceo-insights-localization-industry-25-years-roman-kotzsch/
Krings, H. P. (2001). Repairing texts: Empirical investigations of machine translation post-editing processes (Vol. 5). Kent Ohio & London: Kent State University Press.
Lavington, S. (Ed.) (2012). Alan Turing and his contemporaries: Building the world’s first computers. Swindon: BCS.
Lebedev, M. A., & Nicolelis, M. A. (2017). Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiological Reviews, 97(2), 767–837.
Lee, A. (2023, January 26). ‘What Are Large Language Models Used For?’. Nvidia.comhttps://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/.
Li, B. (2023). Ethical issues for literary translation in the Era of artificial intelligence. Babel, 69(4), 529–545.
Lison, P., Pilán, I., Sánchez, D., Batet, M., & Øvrelid, L. (2021, August). Anonymisation models for text data: State of the art, challenges and future directions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Vol.1., 4188–4203. Online. Association for Computational Linguistics.
Macken, L., Vanroy, B., Desmet, L., & Tezcan, A. (2022). Literary translation as a three-stage process: Machine translation, post-editing and revision. In 23rd Annual Conference of the European Association for Machine Translation. European Association for Machine Translation, 101–110. Ghent, Belgium. European Association for Machine Translation.
Masera, G. (2022). Statistics vs. Machine Learning – and When to Use Either One? Levity.ai, https://levity.ai/blog/statistics-vs-machine-learning
McCulloch, W. & Pitts, W. (1943). A logical calculus of the ideas imminent in nervous activity. Bulletin of Mathematical Biology, 52(1/2), 99–115.
Melby, A. (1996). Machine translation and other translation technologies. Annual Review of Applied Linguistics, 16, 86–98.
ML6.EU. (2023). Large Language Models, https://www.ml6.eu/resources/large-language-models
Moorkens, J. (2020). Translation in the neoliberal era. In E. Bielsa & D. Kapsaskis (Eds.), The Routledge Handbook of Translation and Globalization, pp. 323–336. Abingdon: Routledge.
Muysken, P. (2013). Language contact outcomes as the result of bilingual optimization strategies. Bilingualism: Language and Cognition, 16(4), 709–730.
Nickerson, C. (2005). English as a lingua franca in international business contexts. English for Specific Purposes, 24(4), 367–380.
Pettigrew, T. F. (1998). Intergroup contact theory. Annual Review of Psychology, 49(1), 65–85.
Phillipson, R. (2008). Lingua franca or lingua frankensteinia? English in European integration and globalisation. World Englishes, 27(2), 250–267.
Press, G. (2016). A very short history of Artificial Intelligence (AI). Forbes, https://www.forbes.com/sites/gilpress/2016/12/30/a-very-short-history-of-artificial-intelligence-ai/?sh=33f261376fba
Ramzai, J. (2020). Clearly explained: How machine learning differs from statistical modeling. Towards Data Science, https://towardsdatascience.com/clearly-explained-how-machine-learning-differs-from-statistical-modeling-967f2c5a9cfd.
Ranade, P., Mittal, S., Joshi, A., & Joshi, K. (2018, November). Using deep neural networks to translate multi-lingual threat intelligence. In 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 238–243. Miami, FL, USA.
Robinson, J. A. (1994). Logic, computers, Turing, and von Neumann, in K. Furukawa, D. Michie, and S. Muggleton, Machine Intelligence 13: Machine Intelligence and Inductive Learning, pp. 1–35. Oxford: Clarendon Press.
Rodgers, J. (2017). The Genealogy of an Image, or, What Does Literature (Not) Have To Do with the History of Computing?: Tracing the Sources and Reception of Gulliver’s Knowledge Engine. Humanities, 6(4), 85–93.
Sapiro, G. (2003). The literary field between the state and the market. Poetics, 31(5–6), 441–464.
Scott, B., J. Woods & Chang, A. (2023). How AI could perpetuate racism, sexism and other biases in society. NPR, https://www.npr.org/2023/07/19/1188739764/how-ai-could-perpetuate-racism-sexism-and-other-biases-in-society.
Senavirathne, N., & Torra, V. (2020, December). On the role of data anonymization in machine learning privacy. In 2020 IEEE 19th International conference on trust, security and privacy in computing and communications (TrustCom).IEEE, 664–675. Guangzhou, China.
Shuttleworth, M. (2019). Polysystem theory. In M. Baker & G. Saldanha (Eds.), Routledge Encyclopedia of Translation Studies, pp. 419–423. London: Routledge.
Snell, B. (1978). Introduction [to Translating and the Computer 1978]. https://aclanthology.org/1978.tc1.0.pdf,vi.
Straubhaar, J. D. (1991) Beyond media imperialism: Asymmetrical interdependence and cultural proximity. Critical Studies in Mass Communication, 8(1), 39–59.
Straubhaar, J. D. (2003). Choosing National TV: Cultural capital, language, and cultural proximity in Brazil. In M.G. Elasmar (Ed.), The Impact of International Television: A Paradigm Shift, pp. 77–110. Abingdon/New York: Routledge.
Swift, Jonathan. (1726/1977). Gulliver’s Travels. New York/ Toronto: Oxford University Press.
Toral, A., S. Castilho, K. Hu & Way, A. (2018). Attaining the unattainable? Reassessing claims of human parity in neural machine translation. Proceedings of the Third Conference on Machine Translation: Research Papers [Brussels, Belgium. Association for Computational Linguistics], 113–123.
Toury, G. (1995). Descriptive Translation Studies and Beyond. Amsterdam/Philadelphia, John Benjamins.
Van de Cruys, T. (2022). Constraint-based neural architectures for the translation of literary texts. Paper presented at Network of Interdisciplinary Translation Studies in the Netherlands and Flanders (NITS) Conference. Groningen, The Netherlands.
Van Egdom, G.-W. (2022) Machinevertaling als cultuurpolitiek instrument. Webfilter dossier. https://www.tijdschrift-filter.nl/webfilter/dossier/literair-vertalen-en-technologie/september-2022/machinevertaling-als-cultuurpolitiek-instrument/.
Van Egdom, G. M. W. (2023). Translation criticism in the digital literary sphere: Reader responses to Portuguese literature across the globe and translated literature in Lusophone countries. Qorpus, 13(1), 39–60.
Van Egdom, G.-W., O. Kosters & Declercq, C. (2023). The riddle of (literary) machine translation quality: Assessing automated quality evaluation metrics in a literary context. Tradumatica, 21, 129–159.
Van Massenhove, E., D. Shterionov & Way, A. (2019). Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation. Proceedings of the 17th Machine Translation Summit [MTSummit2019, Dublin, Ireland, August 2019].
Van Voorst, S. (1997). Weten wat er in de wereld te koop is. Goed beeld van liberalisering in NL. The Hague: SDU.
Vashee, K. (2023). Making generative AI effectively multilingual at scale. ModernMT.com blog, https://blog.modernmt.com/making-generative-ai-multilingual-at-scale/.
Verma, C., S. Arora & Jain, A. (2020). Machine learning in healthcare: Separating myth from reality. International Journal of Advanced Science and Technology, 29(8), 1348–1355.
Visbal, O. (2009). The erosion of stereotypes through intercultural exchange programs: Testing Pettigrew´s contact theory. University of Hamburg: PhD Dissertation.
Zhang, C. & Cai, H. (2015). On technological turn of translation studies: Evidences and influences. Journal of Language Teaching and Research, 6(2), 429–434.