AI Methodology Map. Practical and Theoretical Approach to Engage with GenAI for Digital Methods Research
DOI:
https://doi.org/10.6092/issn.1971-8853/19566Keywords:
Generative Artificial Intelligence, GenAI, Digital Methods, AI in Education, Image Networks, Technicity, Algorithmic Race StereotypesAbstract
This essay accounts for a novel way to explore generative artificial intelligence (GenAI) applications for digital methods research, based on the AI Methodology Map. The map is a pedagogical resource and a theoretical framework designed to structure, visually represent, and explore GenAI web-based applications. As an external object, the map functions as a valuable teaching material and interactive toolkit. As a theoretical framework, it is embodied in a static representation that provides principles for engaging with GenAI. Aligned with digital methods’ practical, technical, and theoretical foundations, the map facilitates explorations and critical examinations of GenAI and is supported by visual thinking and data practice documentation. The essay then outlines the map principles, its system of methods, educational entry points, and applications. The organization is as follows: First, we review GenAI methods, discussing how to access them, and their current uses in social research and the classroom context. Second, we define the AI Methodology Map and unpack the theory it embodies by navigating through the three interconnected methods constituting it: making room for, repurposing and designing digital methods-oriented projects with GenAI. Third, we discuss how the map bridges GenAI concepts, technicity, applications and the practice of digital methods, exposing its potential and reproducibility in educational settings. Finally, we demonstrate the AI Methodology Map’s application, employing a digital methodology to analyze algorithmic race stereotypes in image collections generated by nine prominent GenAI apps. In conclusion, the essay unveils methodological challenges, presenting provocations and critiques on repurposing GenAI for social research. By encompassing practice, materiality and theoretical perspective, we argued that the AI Methodology Map bridges theoretical and empirical engagement with GenAI, serving them together or separately, thus framing the essay’s main contribution. We expect that the AI Methodology Map’s reproducibility will likely lead to further discussions, expanding those we present here.
References
Abid, A., Abdalla, A., Abid, A., Khan, D., Alfozan, A., & Zou, J. (2019). Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild. 2019 ICML Workshop on Human in the Loop Learning. arXiv, 1906.02569. https://doi.org/10.48550/arXiv.1906.02569
Agência Lusa. (2023). Universidades de Portugal, Brasil e Espanha juntam-se para discutir impacto e “transição digital” como resposta aos novos “desafios. Observador, 8 November. https://observador.pt/2023/11/08/universidades-de-portugal-brasil-e-espanha-juntam-se-para-discutir-impacto-e-transicao-digital-como-resposta-aos-novos-desafios/
Amietta, R., Matos, A.F.N., & Guilbault, A. (2023). DEW. https://nerd-life-squad.github.io/about
Anderson, L.W., & Krathwohl, D.R. (2001). A Taxonomy for Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives: Complete Edition. New York, NY: Longman.
Anderson, C., Heinisch, J.S., Deldari, S., Salim, F. D., Ohly, S., David, K., & Pejovic, V. (2023). Toward Social Role-based Interruptibility Management. IEEE Pervasive Computing, 22(1), 59–68. https://doi.org/10.1109/mprv.2022.3229905
Antolak-Saper, N., Beilby, K., Boniface, B., Bui, D., Burgess, P., Cheema, A., Crocco, M., Fordyce, R., Galbraith, K., Lansdell, G., Lim, C., Moore, J., Nathania, A., Nawaz, S., Raveendran, L., Saha, T., Sapsed, C., Shannon, B., Soh, K., Swiecki, Z., Vu, T., Wagstaff, P., Wallingford, E., Wong, P., & Zaid, F. (2023). Guides for Assessment Re(design) and Reform. AI in Education Learning Circle. https://www.ai-learning-circle-mon.com/
Arnheim, R. (1980). A Plea for Visual Thinking. Critical Inquiry, 6(3), 489–497. https://doi.org/10.1086/448061
Arnheim, R., & Grundmann, U. (2001). The Intelligence of Vision: An Interview with Rudolf Arnheim. Cabint Magazine, 26 April. https://www.cabinetmagazine.org/issues/2/grundmann_arnheim.php
Báez, J.M. (2023). Performing Representational Labor: Blackness, Indigeneity, and Legibility in Global Latinx Media Cultures. Feminist Media Studies, 23(5), 2455–2470. https://doi.org/10.1080/14680777.2022.2056755
Baidoo-Anu, D., & Ansah, L.O. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1). https://doi.org/10.1007/s12525-023-00680-1
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362. https://doi.org/10.1609/icwsm.v3i1.13937
Birhane, A. (2022). The Unseen Black Faces of AI Algorithms. Nature, 610(7932), 451–452. https://doi.org/10.1038/d41586-022-03050-7
Boiret, G. (2016). PhantomBuster. [Software]. https://phantombuster.com/
Borra, E. (2023). ErikBorra/PromptCompass (v0.4). Zenodo. https://doi.org/10.5281/zenodo.10252681
Borra, E. (2024). The Medium Is the Methods: Using Large Language Models (LLMs) in Digital Research. [Keynote]. Digital Methods Winter School, University of Amsterdam, Amsterdam, The Netherlands.
Botta, M., Autuori, A., Subet, M., Terenghi, G., Omena, J.J., Leite, E., Kim, F.C. (2024). Designing With: A New Educational Module to Integrate Artificial Intelligence, Machine Learning and Data Visualization in Design Curricula. https://designingwithai.ch/
Bounegru, L., Gray, J., Venturini, T., & Mauri, M. (Eds.). (2018). A Field Guide to ‘Fake News’ and Other Information Disorders. Public Data Lab. https://doi.org/10.2139/ssrn.3097666
Bunz, M. [GoetheUK]. (2022). The Culture of Artificial Intelligence. Goethe Annual Lectures at the Goethe-Institut London. [Video]. YouTube, 24 November. https://www.youtube.com/watch?v=bTR6EP34W_w
Buolamwini, J.A. (2017). Gender Shades: Intersectional Phenotypic and Demographic Evaluation of Face Datasets and Gender Classifiers. [Doctoral dissertation, University of Missouri]. Massachusetts Institute of Technology, Cambridge.
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 77–91.
Burkhardt, S., & Rieder, B. (2024). Foundation Models are Platform Models: Prompting and the Political Economy of AI. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241247839
Castro, J.C.M., & Shumsher, S. (2023). Situating Gen-AI Pain & Pleasure: Interpretative Querying Approach Combining Situational Analysis with Digital Methods [Presentation slides]. Faculdade de Ciências Sociais e Humanas, Universidade NOVA de Lisboa. http://dx.doi.org/10.13140/RG.2.2.16436.67201
Chao, J. (2021). Memespector GUI: Graphical User Interface Client for Computer Vision APIs (Version 0.2.5 beta). [Software]. https://github.com/jason-chao/memespector-gui
Chauhan, A., Anand, T., Jauhari, T., Shah, A., Singh, R., Rajaram, A., & Vanga, R. (2024). Identifying Race and Gender Bias in Stable Diffusion AI Image Generation. 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC), 1–6. https://doi.org/10.1109/ICAIC60265.2024.10433840
Ciston, S. (2023). A Critical Field Guide for Working with Machine Learning Datasets. https://knowingmachines.org/critical-field-guide
Colombo, G., De Gaetano, C., & Niederer, S. (2023). Prompting For Biodiversity: Visual Research With Generative AI. Digital Methods Summer School 2023. https://wiki.digitalmethods.net/Dmi/PromptingForBiodiversity
Cross, N. (2001). Designerly Ways of Knowing: Design Discipline versus Design Science. Design Issues, 17(3), 49–55. https://doi.org/10.1162/074793601750357196
Dąbkowski, P., & Staniszewski, M. (2022). ElevenLabs. https://elevenlabs.io/
Dayma, B. (2022). Crayon (v3). https://www.craiyon.com/
de Seta, G., Pohjonen, M., & Knuutila, A. (2023). Synthetic Ethnography: Field Devices for the Qualitative Study of Generative Models. SocArXiv. https://doi.org/10.31235/osf.io/zvew4
Dove, G., Halskov, K., Forlizzi, J. & Zimmerman, J. (2017). UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 278–288). ACM Press. https://dl.acm.org/doi/10.1145/3025453.3025739
Duguay, S., & Gold-Apel, H. (2023). Stumbling Blocks and Alternative Paths: Reconsidering the Walkthrough Method for Analyzing Apps. Social Media + Society, 9(1). https://doi.org/10.1177/20563051231158822
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A., Baabdullah, A.,M., Koohang, A., Raghavan, V., Ahuja,M., Albanna, H., Albashrawi, M.A., Al-Busaidi, A.S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L., Chowdhury, S., Crick, T., Cunningham, S.W., Davies, G.H., Davison, R.M., Dé, R., Dennehy, D., Duan, Y., Dubey, R., Dwivedi, R., Edwards, J.S., Flavián, C., Gauld, R., Grover, V., Hu, M.-C., Janssen, M., Jones, P., Junglas, I., Khorana, S., Kraus, S., Larsen, K.R., Latreille, P., Laumer, S., Malik, F.T., Mardani, A., Mariani, M., Mithas, S., Mogaji, E., Nord, J.H., O’Connor, S., Okumus, F., Pagani, M., Pandey, N., Papagiannidis, S., Pappas, I.,O., Pathak, N., Pries-Heje, J., Raman, R., Rana, N.P., Rehm, S.-V., Ribeiro-Navarrete, S., Richter, A., Rowe, F., Sarker, S., Carsten Stahl, S., Kumar Tiwari, M., van der Aalst, W., Venkatesh, V., Viglia, G., Wade, M., Walton, P., Wirtz, J., & Wright, R. (2023). “So What if ChatGPT Wrote it?” Multidisciplinary Perspectives on Opportunities, Challenges and Implications of Generative Conversational AI for Research, Practice and Policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Edkie, A., Pandey, D., & Roy, S. (2020). Murf.AI. https://murf.ai/
Farooq, M., Buzdar, H. Q. & Muhammad, S. (2023). AI-Enhanced Social Sciences: A Systematic Literature Review and Bibliographic Analysis of Web of Science Published Research Papers. Pakistan Journal of Society, Education and Language (PJSEL), 10(1), 250–267.
Ferrara, E. (2024). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci, 6(1). https://doi.org/10.3390/sci6010003
Franklin, U. (1990). The Real World of Technology. Toronto: CBC.
García-Peñalvo, F., & Vázquez-Ingelmo, A. (2023). What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7. https://doi.org/10.9781/ijimai.2023.07.006
Gaspar, B. (2023). Cientistas divulgam 10 diretrizes para a Educação lidar com a Inteligência Artificial. Fepesp - Federação Dos Professores Do Estado de São Paulo. https://fepesp.org.br/noticia/cientistas-divulgam-10-diretrizes-para-a-educacao-lidar-com-a-inteligencia-artificial/
Google Creative Lab (2017). Teachable Machine. [software]. https://teachablemachine.withgoogle.com/
Gorska, A.M., & Jemielniak, D. (2023). The Invisible Women: Uncovering Gender Bias in AI-generated Images of Professionals. Feminist Media Studies, 23(8), 4370–4375. https://doi.org/10.1080/14680777.2023.2263659
Goulart, J. (2024). Silvio Meira: ‘Estamos na era da pedra lascada da IA, mas o futuro chega em 800 dias’. Brazil Journal, 16 March. https://braziljournal.com/silvio-meira-estamos-na-era-da-pedra-lascada-da-ia-mas-o-futuro-chega-em-800-dias/
Gray, J., Bounegru, L., Rogers, R., Venturini, T., Ricci, D., Meunier, A., Mauri, M., Niederer, S., Sánchez Querubín, N., Tuters, M., Kimbell, L., & Munk, K. (2022). Engaged Research-led Teaching: Composing Collective Inquiry with Digital Methods and Data. Digital Culture & Education, 14(3), 55–86.https://www.digitalcultureandeducation.com/volume-14-3
Graziani, M., Dutkiewicz, L., Calvaresi, D., Amorim, J. P., Yordanova, K., Vered, M., Nair, R., Henriques Abreu, P., Blanke, T., Pulignano, V., Prior, J.O., Lauwaert, L., Reijers, W., Depeursinge, A., Andrearczyk, V., & Müller, H. (2023). A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences. Artificial Intelligence Review, 56(4), 3473–3504. https://link.springer.com/article/10.1007/s10462-022-10256-8
Greene, C. (2023). AI and the Social Sciences: Why All Variables are Not Created Equal. Res Publica, 29(2), 303–319. https://doi.org/10.1007/s11158-022-09544-5
Hartman, J., Schwenzow, J., & Witte, M. (2023). The Political Ideology of Conversational AI: Converging Evidence on ChatGPT’s Pro-environmental, Left-libertarian Orientation. arXiv. https://doi.org/10.2139/ssrn.4316084
Hoel, A. S. (2012). Technics of Thinking. In A.S. Hoel & I. Folkvord (Eds.), Ernst Cassirer on Form and Technology: Contemporary Readings (pp. 65–91). London: Palgrave Macmillan.
Honig, C., Rios, S., & Oliveira, E. (2023). A Tool for Learning: Classroom Use-cases for Generative AI. The Chemical Engineer, 1 June. https://www.thechemicalengineer.com/features/a-tool-for-learning-classroom-use-cases-for-generative-ai/
Hooks, B. (1992). Black Looks: Race and Representation. Boston, MA: End Press.
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS ONE, 9(6), e98679. https://doi.org/10.1371/journal.pone.0098679
Kendall, A., Grimes, M., & Cipolla, R. (2016). PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization. arXiv. https://doi.org/10.1109/ICCV.2015.336
Koplin, J.J. (2023). Dual-use Implications of AI text Generation. Ethics and Information Technology, 25(2), 32. https://doi.org/10.1007/s10676-023-09703-z
Leshkevich, T., & Motozhanets, A. (2022). Social Perception of Artificial Intelligence and Digitization of Cultural Heritage: Russian Context. Applied Sciences, 12(5), 2712. https://doi.org/10.3390/app12052712
Limewire. (2023). BlueWillow. [Software]. https://www.bluewillow.ai/
Luccioni, A.S., Akiki, C., Mitchell, M., & Jernite, Y. (2023). Stable Bias: Evaluating Societal Representations in Diffusion Models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems (pp. 56338–56351). New York, NY: Curran Associates.
Maier, N., Parodi, F., & Verna, S. (2004). DownThemAll! (v4.12.1). [Web browser plugin]. https://www.downthemall.org/
Manovich, L. (2013). Museum Without Walls, Art History Without Names: Methods and Concepts for Media Visualization. In C. Vernallis, A. Herzog & J. Richardson (Eds.), The Oxford Handbook of Sound and Image in Digital Media (pp. 252–278). Oxford: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199757640.013.005
Manovich, L. (2020). Cultural Analytics. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/11214.001.0001
Marres, N. (2017). Digital Sociology: The Reinvention of Social Research. London: Wiley.
Mauri, M. & Ciuccarelli, P. (2016). Designing Diagrams for Social Issues. Future Focused Thinking - DRS International Conference 2016. https://doi.org/10.21606/drs.2016.185
Mauri, M., Briones, M.A., Gobbo, B. & Colombo, G. (2020). Research Protocol Diagrams as Didatic Tools to Act Critically in Dataset Design Processes. INTED2020 Proceedings, (pp. 9034–9043). https://doi.org/10.21125/inted.2020.2470
Microsoft. (2023). Bing Image Creator. https://www.bing.com/images/create
Midjourney Inc. (2022). Midjourney (Version 5.2). https://www.midjourney.com/
Mostaque, E. (2019). Stability.ai. https://stability.ai/
Nicoletti, L., & Bass, D. (2023). Humans Are Biased. Generative AI Is Even Worse. Bloomberg, 9 June. https://www.bloomberg.com/graphics/2023-generative-ai-bias/
Noble, U.S. (2013). Google Search: Hyper-visibility as a Means of Rendering Black Women and Girls Invisible. InVisible Culture, 19. https://doi.org/10.47761/494a02f6.50883fff
Noble, S.U. (2018). Algorithms of Oppression. New York, NY: New York University Press. https://doi.org/10.18574/nyu/9781479833641.001.0001
Omena, J.J. (2021a). Digital Methods and Technicity-of-the-Mediums. From Regimes of Functioning to Digital Research. [Doctoral Dissertation, Universidade NOVA de Lisboa]. http://hdl.handle.net/10362/127961
Omena, J.J., Pilipets, E., Gobbo, B., & Chao, J. (2021b). The Potentials of Google Vision API-based Networks to Study Natively Digital Images. Revista Diseña, 19. https://doi.org/10.7764/disena.19.article.1
Omena, J.J. (2022). Technicity-of-the-mediums. In A. Ceron (Ed.), Elgar Encyclopedia of Technology and Politics (pp. 77–81). Cheltenham: Elgar.
OpenAI. (2023). ChatGPT. Large Language Model. OPenAI. https://chat.openai.com
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Leoni Aleman, F., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, R., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Won Chung, H., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Posada Fishman, S., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Shane Gu, S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Kaiser, Ł., Kamali, A., Kanitscheider, I., Shirish Keskar, N., Khan, T., Kilpatrick, L., Wook Kim, J., Kim, C., Kim, Y., Kirchner, J.H., Kiros, J., Knight, M., Kokotajlo, D., Kondraciuk, Ł., Kondrich, A., Konstantinidis, Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, A.D., Ming Li, C., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A., Malfacini, K., Manning, S., Markov, T., Markovski,Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., Mayer McKinney, S., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D., Mu, T., Murati, M., Murk, O., Mély, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Ouyang, L., O’Keefe, C., Pachocki, J.,Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., de Avila Belbute Peres, F., Petrov, M., Ponde de Oliveira Pinto, H., Rai Pokorny, .M., Pokrass, M., Pong, V.,H., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Cameron Raymond, Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B., Song, Y., Staudacher, N., Such, F.P., Summers, N., Sutskever, I., Tang, J., Tezak, N., Thompson, M.B., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Cerón Uribe, F.J., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J.J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C.J., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., & Zoph, B. (2024). GPT-4 Technical Report. arXiv. http://arxiv.org/abs/2303.08774
Pask, G. (1975). Minds and Media in Education and Entertainment: Some Theoretical Comments Illustrated by the Design and Operation of a System for Exteriorizing and Manipulating Individual Theses. In R. Trappl & G. Pask (Eds.), Progress in Cybernetics and System Research (pp. 38–50). London: Hemisphere.
Peeters, S. (2023). Zeeschuimer (Version 1.4). [Firefox plugin]. https://doi.org/10.5117/CCR2022.2.007.HAGE
Perez, J., Castro, M., & Lopez, G. (2023). Serious Games and AI: Challenges and Opportunities for Computational Social Science. IEEE Access, 11, 62051–62061. https://doi.org/10.1109/ACCESS.2023.3286695
Peeters, S., & Hagen, S. (2022). The 4CAT Capture and Analysis Toolkit: A Modular Tool for Transparent and Traceable Social Media Research. Computational Communication Research, 4(2), 571–589. https://computationalcommunication.org/ccr/article/view/120
Popescu, A., & Schut, A. (2023). Generative AI in Creative Design Processes: aDive into Possible Cognitive Biases. In D. De Sainz Molestina, L. Galluzzo, F. Rizzo & D. Spallazzo (Eds.), IASDR 2023: Life-Changing Design (pp. 1–10). https://doi.org/10.21606/iasdr.2023.784
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv. http://arxiv.org/abs/2204.06125
Rieder, B. (2020). Engines of Order: a Mechanology of Algorithmic Techniques. Amsterdam: Amsterdam University Press.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. arXiv. https://doi.org/10.1109/CVPR52688.2022.01042
Rogers, R. (2013). Digital Methods. Cambridge, MA: MIT Press.
Rogers, R. & Lewthwaite, S. (2019). Teaching Digital Methods: Interview. with Richard Rogers. Revista Diseña, 14, 12–37. https://doi.org/10.7764/disena.14.12-37
Rogers, S., & Cairo, A. (2022). TwoTone. https://twotone.io/
Rozado, D. (2023). The Political Biases of ChatGPT. Social Sciences, 12(3), 148. https://doi.org/10.3390/socsci12030148
Russell Group. (2023). New Principles on Use of AI in Education. The Russell Group, 4 June. https://russellgroup.ac.uk/news/new-principles-on-use-of-ai-in-education/
Salvaggio, E. (2022). How to Read an AI Image. Cybernetic Forests, 2 October. https://www.cyberneticforests.com/news/how-to-read-an-ai-image
Shameem, S. (2022). Lexica AI. https://lexica.art/
Sharma, S. (2024). Understanding Digital Racism: Networks, Algorithms, Scale. Lanham, MD:Rowman & Littlefield.
Shrestha, Y.R., von Krogh, G., & Feuerriegel, S. (2023). Building Open-Source AI. Nature Computational Science, 3, 908–911 http://dx.doi.org/10.2139/ssrn.4614280
Silva, T. (2023). Mapeamento de Danos e Discriminação Algorítmica. Desvelar. https://desvelar.org/casos-de-discriminacao-algoritmica/
Sinclair, D., Dowdeswell, T., & Goltz, N. (2023). Artificially Intelligent Sex Bots and Female Slavery: Social Science and Jewish Legal and Ethical Perspectives. Information & Communications Technology Law, 32(3), 328–355. https://doi.org/10.1080/13600834.2022.2154050
Sinclair, S., & Rockwell, G. (2003). Voyant Tools (v2.6.13). [Software]. https://voyant-tools.org/
Souza, R. [@renatasouzario]. (2023). Racismo nas plataformas de inteligência artificial! [Video]. Instagram, 25 October. https://www.instagram.com/reel/Cy1p6EQpwXB/?igshid=MzRlODBiNWFlZA%3D%3D
Stokel-Walker, C., & Van Noorden, R. (2023). What ChatGPT and Generative AI Mean for Science. Nature, 614(7947), 214–216. https://doi.org/10.1038/d41586-023-00340-6
Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2023). Smiling Women Pitching Down: Auditing Representational and Presentational Gender Biases in Image Generative AI. arXiv. https://doi.org/10.1093/jcmc/zmad045
The DigiKam Team. (2001). digiKam (v8.3.0). [Software]. https://www.digikam.org/
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Canton Ferrer, C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Singh Koura, P., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, M., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E.M., Subramanian, R., Tan, X.E., Tang, B., Taylor, R., Williams, A., Kuan, J.X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., & Scialom, T. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv. http://arxiv.org/abs/2307.09288
Valenzuela, C., Matamala, A., & Germanidis, A. (2018). RunwayML. https://runwayml.com/
Visual Computing Group. (2018). Image Sorter (v4). [Software]. https://visual-computing.com/project/imagesorter/
Visual Crossing Corporation. (2003). Visual Crossing. https://www.visualcrossing.com/
Vogel, K.M. (2021). Big Data, AI, Platforms, and the Future of the U.S. Intelligence Workforce: A Research Agenda. IEEE Technology and Society Magazine, 40(3), 84–92. https://doi.org/10.1109/MTS.2021.3104384
Wang, F.-Y., Ding, W., Wang, X., Garibaldi, J., Teng, S., Imre, R., & Olaverri-Monreal, C. (2022). The DAO to DeSci: AI for Free, Fair, and Responsibility Sensitive Sciences. IEEE Intelligent Systems, 37(2), 16–22. https://doi.org/10.1109/MIS.2022.3167070
Wojcicki, A. (2020). Artflow AI. https://app.artflow.ai
Yu, C., Tschanz-Egger, J.L., & Souto, M. (2023). Tomato Girl Summer. Designing With: A New Educational Module to Integrate Artificial Intelligence, Machine Learning and Data Visualization in Design Curricula, 19 June. https://master-interaction-design.notion.site/Tomato-Girl-Summer-07dcf86e607e44d5b00b5d8cd9524a75
Zajko, M. (2021). Conservative AI and Social Inequality: Conceptualizing Alternatives to Bias through Social Theory. AI and Society, 36(3), 1047–1056. https://doi.org/10.1007/s00146-021-01153-9
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Janna Joceli Omena, Antonella Autuori, Eduardo Leite Vasconcelos, Matteo Subet, Massimo Botta
This work is licensed under a Creative Commons Attribution 4.0 International License.
