El espejo algorítmico: miradas desde las ciencias sociales en torno a la interacción entre humanos y la inteligencia artificial (IA)

Adhikari, Prakash (2024). “Exploring the Nexus between Artificial Inte- lligence and Job Displacement: A Literature Review”., Journal of National Development, núm. 37(1), pp. 1-13. doi: doi.org/10.62047/ JND.2024.06.30.1

Alaniz, Teresa (2025). “The Emotional Economy: How Emotional Ex- periences are Becoming the Next Frontier”, Bootcamp. Disponible en: https://medium.com/design-bootcamp/the-emotional-eco- nomy-how-emotional-experiences-are-becoming-the-next-fron- tier-dfbfacfdd8b9

An, Jiafu, Difang Huang, Chen Lin y Mingzhu Tai (2024). “Measuring Gender and Racial Biases in Large Language Models”, pnas Nexus, vol. 4, núm. 3. doi: doi.org/10.48550/arXiv.2403.15281

Bago, Bence y Jean-François Bonnefon (2024). “Generative ai as a Tool for Truth”, Science, núm. 385(6714), pp. 1164-1165. DOI: doi. org/10.1126/science.ads0433

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major y Shmarga- ret Shmitchell (2021). “On the Dangers of Stochastic Parrots: Can Language Models Be too Big?”, Proceedings of the 2021 acm Conference on Fairness, Accountability, and Transparency.

Choi, J. y Nixon, Nia (2025). “Agentic Men, Communal Women? Explo- ring Gender Bias in llm-based Leadership Identification for Colla- boration Analytics”, en Alexandra I. Cristea, Erin Walker, Yu Lu, Olga C. Santos y Seiji Isotani (eds.). Artificial Intelligence in Education. Palermo: Springer, pp. 11-18.

Data Workers’ Inquiry (s.f.). Distributed ai Research Center. Disponible en: https://data-workers.org/

Depounti, Iliana y Simone Natale (2025). “Decoding Artificial Sociality: Technologies, Dynamics, Implications”, New Media & Society, núm. 27(10), pp. 5457-5470. doi: doi.org/10.1177/14614448251359217

Eloundou, Tyna, Sam Manning, Pamela Mishkin y Daniel Rock (2023). “gpts are gpts: An Early Look at the Labor Market Impact Potential of Large Language Models”, OpenAI/ArXiv.

Fang, Cathy M., Auren R. Liu, Valdemar Danry, Eunhaer Lee et al. (2025). “How ai and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study”, ArXiv. doi: doi.org/10.48550/arXiv.2503.17473

Gunkel, David J. (2025). ai for Communication. Londres: Routledge.

Guo, Yanzhu, Simone Conia, Zelin Zhou, Min Li, Saloni Potdar y Henry Xiao (2024). Do Large Language Models Have an English Accent? Eva- luating and Improving the Naturalness of Multilingual LLMs. doi: doi.org/10.48550/arXiv.2410.15956

Guzman, Andrea L. (2018). “What is Human-Machine Communication, Anyway?”, en Anadrea L. Guzman (ed.). Human-Machine Communica- tion: Rethinking Communication, Technology, and Ourselves. Lausana: Peter Lang, pp. 1-29.

Hanna, John J., Abdi D. Wakene, Andrew O. Johnson, Christoph U. Le- hmann y Richard J. Medford (2025). “Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Ca- re-Related Tasks: Cross-Sectional Study”, Journal of Medical Internet Research, núm. 27. doi: doi.org/10.2196/57257

Hancock, Jeffrey T., Mor Naaman y Karen Levy (2020). “ai-Mediated Communication: Definition, Research Agenda, and Ethical Consi- derations”, Journal of Computer-Mediated Communication, núm. 25(1), pp. 89-100. https://doi.org/10.1093/jcmc/zmz022

Hepp, Andreas, Wiebke Loosen, Stephan Dreyer, Juliana Jarke et al. (2023). “Chatgpt, Lamda, and the Hype Around Communicative ai: The Automation of Communication as a Field of Research in Media and Communication Studies”, Human-Machine Communication, núm. 6, pp. 41-63. doi: doi.org/10.30658/hmc.6.4

Horvitz, Eric y Tom . Mitchell (2024). “Scientific Progress in Artificial Intelligence: History, Status, and Futures”, en Kathleen Hall Jamie- son, Anne-Marie Mazza y William Kearney (eds.). Realizing the Pro- mise and Minimizing the Perils of AI for Science and the Scientific Community. Filadelfia: University of Pennsylvania Press, pp. 147-193.

Hu, Krystal (2023, febrero 2). “Chatgpt Sets Record for Fastest-Growing User base - Analyst Note”, Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-no-te-2023-02-01/Iannaccone,

Sandro (2024). “Cómo funcionan las redes neuronales detrás de la IA premiadas con el Nobel de Física 2024”, Wired. Disponible en: https://es.wired.com/articulos/como-funcionan-redes-neuro- nales-detras-de-ia-premiadas-con-nobel-de-fisica-2024

Instituto Nacional de Estadística y Geografía (2025). “Encuesta Nacional de Victimización y Percepción sobre Seguridad Pública (envipe), In- forme de prensa, núm. 127. Disponible en: https://www.inegi.org.mx/ contenidos/saladeprensa/boletines/2025/envipe/envipe_25.pdf

Kaplan, Jerry (2024). Generative Artificial Intelligence: What Everyone Needs to Know. Oxford: Oxford University Press.

Kew, Tannon, Florian Schottmanny RicoSennrich (2024). “Turning English-Centric llms into Polyglots: How Much Multilinguality Is Needed?”, En Yaser Al-Onaizan, Mohit Bansaly Yun Nung Chen (eds.). Findings of the Association for Computational Linguistics. emnlp, pp. 13097-13124.

Kosmyna, Nataliya Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian Hao Liao, Ashly Vivian Beresnitzky, IrisBraunstein y Pattie Maes (2025). Your Brain on Chatgpt: Accumulation of Cognitive Debt when Using an ai Assistant for Essay Writing Task. DOI: doi.org/10.48550/ arXiv.2506.08872

Lai, Emily R. (2011). Critical Thinking: A Literature Review. Pearson’s Re- search Reports, 6(1), pp. 40-41.

Li, Meng (2024, noviembre 16). “Hinton: Forget AI, Become a Plumber!”, AI Disruption. Disponible en: https://medium.com/ai-disruption/ hinton-forget-ai-become-a-plumber-c831603f5e8b

Liu, Zhaoming (2024). “Cultural Bias in Large Language Models: A Com- prehensive Analysis and Mitigation Strategies”, Journal of Transcultural Communication. doi: doi.org/10.1515/jtc-2023-0019

Natale, Simone (2021). Deceitful Media: Artificial Intelligence and Social Life after the Turing Test. Oxford: Oxford University Press.

Navigli, Roberto Simone Coniay Benedetta Ross (2023). “Biases in Large Language Models: Origins, Inventory and Discussion”, acm Journal of Data and Information Quality, pp. 1-21. DOI: doi.org/10.1145/3597307

Otaki, Bunichi (2023). “Feedback in the Era of Generative AI”. Tesis de maestría. Universidad de Gothenburgo.

Posada, Julián (2022). “The Coloniality of Data Work: Power and Inequa- lity in Outsourced Data Production for Machine Learning”, tesis de doctorado. Toronto: University of Toronto.

— Gemma Newlandsy Milagros Miceli (2023). “Labor, automation, and human-machine communication”, en Andrea L. Guzman, Ste- ve Jones y Rhonda McEwen (eds.). The Sage Handbook of Human-Ma- chine Communication. Sage, pp. 384-391.

Radford, Alec, Karthik Narasimhan, Tim Salimans e Ilya Sutskever. (2018). Improving Language Understanding by Generative Pre-training. Disponible en: https://cdn.openai.com/research-covers/language-un- supervised/language_understanding_paper.pdf

Reuters (2025, diciembre 17). “Openai Discussed Raising Tens of Bi- llions at About $750 Billion Baluation, the Information Reports”. Disponible en: https://www.reuters.com/technology/openai-dis- cussed-raising-tens-billions-valuation-about-750-billion-informa- tion-2025-12-18/

Schiller, Dan (1999). Digital Capitalism: Networking the Global Market System. Cambridge: mit Press.

Seaver, Nick (2017). “Algorithms as Culture”, Big Data & Society, núm. 4(2), pp. 1-12.

Seth, Agrima, Monojit Choudhury, Sunayana Sitaram, Kentaro Toyama, Aditya Vashistha y Kalika Bali (2025). “How Deep Is Representatio- nal Bias in llms? The Cases of Caste and Religion”, Proceedings of the aaai/acm Conference on ai, Ethics, and Society, núm. 8(3), pp. 2319-2330. doi: doi.org/10.1609/aies.v8i3.36718

Susskind, Richard y Daniel Susskind (2015). The Future of the Professions. Oxford: Oxford University Press.

Turkle, Sherry (2011). Alone Together: Why We Expect More from Technology and Less froE Other. Basic Books.

Vizcaya, Emmanuel (2025, 21 de enero). Aproximaciones al artificeno, núm. 404. Disponible en: https://centroculturadigital.mx/revista/apro- ximaciones-al-artificeno

Williams, Adrienne, Milagros Miceli, y Timnit Gebru (2022), The explo- teid Labor Behind Artificial Intelligence”, Noema. Disponible en: https://www.noemamag.com/the-exploited-labor-behind-artifi- cial-intelligence

Zhuang, Yan (2025). “Why We Tell ai Our Stories: Exploring Motiva- tions, Perceptions, and Impact of Interactions with Chatgpt”. Tesis de maestría. Uppsala: Uppsala Universitet. Disponible en: https:// www.diva-portal.org/smash/get/diva2:1976495/fulltext02.pdf