25,089
Views
14
CrossRef citations to date
0
Altmetric
Research Articles

Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists

Pages 177-193 | Received 05 Feb 2021, Published online: 18 Aug 2022

References

  • Adams, W.C. (2015) Conducting Semi-Structured Interviews. In J. S. Wholey, H. P. Harty, & K. E. Newcomer (Eds.), Handbook of Practical Program Evaluation (pp. 492–505). Hoboken, NJ: Jossey-Bass.
  • Agnoli, S., Corazza, G. E., & Runco, M. A. (2016). Estimating creativity with a multiple-measurement approach within scientific and artistic domains. Creativity Research Journal, 28(2), 171–176. doi:10.1080/10400419.2016.1162475
  • Amabile, T. M. (1996). Creativity in context: Update to the social psychology of creativity. New York, NY: Westview Press.
  • Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39(5), 1154–1184. doi:10.5465/256995
  • Anantrasirichai, N., & Bull, D. (2022). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55, 89–656. doi:10.1007/s10462-021-10039-7
  • Baer, J. (2010). Is creativity domain-specific? In J. C. Kaufman & R. J. Sternberg (Eds.), Cambridge Handbook of Creativity (pp. 321–341). New York, NY: Cambridge University Press.
  • Baer, J. (2013). Teaching for creativity: Domains and divergent thinking, intrinsic motivation, and evaluation. In M. Gregerson, J. Kaufman, & H. Snyder (Eds.), Teaching creatively and teaching creativity (pp. 175–181). Springer.
  • Baer, J., & Kaufman, J. C. (2005). Bridging generality and specificity: The amusement park theoretical (APT) model of creativity. Roeper Review, 27(3), 158–163. doi:10.1080/02783190509554310
  • Batey, M., & Furnham, A. (2006). Creativity, intelligence, and personality: A critical review of the scattered literature. Genetic, Social, and General Psychology Monographs, 132(4), 355–429. doi:10.3200/MONO.132.4.355-430
  • Berman, A., & James, V. (2018). Learning as performance: Autoencoding and generating dance movements in real time. In A. Liapis, J. J. R. Cardalda, & A. Ekárt (Eds.), International Conference on Computational Intelligence in Music, Sound, Art and Design (pp. 256–266). Springer. doi:10.1007/978-3-319-77583-8.
  • Biggs, S. (2009). New media: The “first word in art? In H. Smith & E. T. Dean (Eds.), Practice-led research, research-led practice in the creative arts (pp. 66–83). Edinburgh University Press.
  • Boden, M. A. (2004). The creative mind: myths and mechanisms (2nd ed.). New York, NY: Routledge.
  • Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30(3), 23–34. doi:10.1609/aimag.v30i3.2254
  • Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.
  • Botella, M., Glaveanu, V., Zenasni, F., Storme, M., Myszkowski, N., Wolff, M., & Lubart, T. (2013). How artists create: Creative process and multivariate factors. Learning and Individual Differences, 26, 161–170. doi:10.1016/j.lindif.2013.02.008
  • Bray, L., & Bown, O. (2016). Applying core interaction design principles to computational creativity. In F. Pachet, A. Cardoso, V. Corruble, & F. Ghedini (Eds.), Proceedings of the Seventh International Conference on Computational Creativity (ICCC 206) (pp. 93–97). Paris, France: Sony CSL.
  • Bringsjord, S., Bello, P., & Ferrucci, D. (2001). Creativity, the turing test, and the (better) Lovelace test. Minds and Machines, 11(1), 3–27. doi:10.1023/A:1011206622741
  • Browne, K. (2022). Who (or What) Is an AI Artist? Leonardo, 55(2), 130–134. doi:10.1162/leon_a_02092
  • Bullot, N. J., Seeley, W. P., & Davies, S. (2017). Art and science: A philosophical sketch of their historical complexity and codependence. The Journal of Aesthetics and Art Criticism, 75(4), 453–463. doi:10.1111/jaac.12398
  • Canaan, R., Menzel, S., Togelius, J., & Nealen, A. (2018). Towards game-based metrics for computational co-creativity. In C. Browne, M. H. M. Winands, J. Liu, & M. Preuss (Eds.), Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18) (pp. 482–489). Maastricht, The Netherlands.
  • Choi, K., Fazekas, G., & Sandler, M. (2016). Text-based LSTM networks for automatic music composition [Paper presentation]. In 1st Conference on Computer Simulation of Musical Creativity (CSMC 16), Huddersfield, United Kingdom. arXiv:1604.05358
  • Chung, N. C. (2021). Human in the loop for machine creativity. In 9th AAAI Conference on Human Computationand Crowdsourcing (HCOMP 2021), Virtual conference.arXiv:2110.03569.
  • Colin, T. R., Belpaeme, T., Cangelosi, A., & Hemion, N. (2016). Hierarchical reinforcement learning as creative problem solving. Robotics and Autonomous Systems, 86, 196–206. doi:10.1016/j.robot.2016.08.021
  • Colton, S. (2012). The painting fool: Stories from building an automated painter. In J. McCormack & M. d’Inverno (Eds.), Computers and creativity (pp. 3–38). Springer.
  • Colton, S., & Wiggins, G., A. (2012). Computational creativity: The final frontier? In L. De Raedt, C. Bessiere, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, & P. Lucas (Eds.), Proceedings of the 20th European Conference on Artificial Intelligence (ECAI) (pp. 21–26). Amsterdam, The Netherlands: IOS Press. doi:10.3233/978-1-61499-098-7-21
  • Cropley, A. (2020). Creativity-focused technology education in the age of industry 4.0. Creativity Research Journal, 32(2), 184–191. doi:10.1080/10400419.2020.1751546
  • Csikszentmihalyi, M. (1997). Creativity: Flow and the psychology of discovery and invention. New York, NY: HarperPerennial.
  • Csikszentmihalyi, M. (2014). Society, culture, and person: A systems view of creativity. In M. Csikszentmihalyi (Ed.), The systems model of creativity (pp. 47–61). Dordrecht, The Netherlands: Springer. doi:10.1007/978-94-017-9085-7_4
  • Csikszentmihalyi, M., & Sawyer, K. (2014). Creative insight: The social dimension of a solitary moment. In M. Csikszentmihalyi (Ed.), The systems model of creativity (pp. 73–98). Dordrecht, The Netherlands: Springer. doi:10.1007/978-94-017-9085-7_7
  • Daniele, A., & Song, Y. (2019). AI art = human. In V. Conitzer, G. K. Hadfield, & S. Vallor (Eds.), Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 155-161). New York, NY: Association for Computing Machinery. doi:10.1145/3306618.3314233
  • Davis, N. M. (2013). Human-computer co-creativity: Blending human and computational creativity. In G. Smith & A. Smith (Eds.), The Doctoral Consortium at AIIDE 2013 (pp. 9–12). The AAAI Press.
  • De Garrido, L. (2021). Conceptual design of a creative artificial intelligence system based on the neurocognitive bases of human creativity in the brain. Creativity Research Journal, 1–22. doi:10.1080/10400419.2021.2005309
  • Dvorsky, G. (2017). This artificially intelligent robot composes and performs its own music. Gizmodo. Accessed 1 March 2022. https://gizmodo.com/this-artificially-intelligent-robot-composes-and-perfor-1796093082
  • Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). Can: Creative adversarial networks, generating” art” by learning about styles and deviating from style norms. In A. Goel, A. Jordanous, & A. Pease (Eds.), Proceedings of the 8th International Conference on Computational Creativity (ICCC’17) (pp.96–103). Georgia Institute of Technology.
  • Elliott, A. (2019). The culture of AI: Everyday life and the digital revolution. Abingdon, UK: Routledge.
  • Esling, P., & Devis, N. (2020). Creativity in the era of artificial intelligence. In JIM Conference 2020. Journées d’Informatique Musicale, Strasbourg, France. doi:10.48550/arXiv.2008.05959
  • Fabiano, F., Pelikan, H. R., Pingen, J., Zissoldt, J., Catala, A., & Theune, M. (2017). Designing a co-creative dancing robotic tablet [Paper presentation]. In The 6th International Workshop on Computational Creativity, Concept Invention, and General Intelligence (C3GI 2017), Madrid, Spain.
  • Feist, G. J. (1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review, 2(4), 290–309. doi:10.1207/s15327957pspr0204_5
  • Fischer, G., Giaccardi, E., Eden, H., Sugimoto, M., & Ye, Y. (2005). Beyond binary choices: Integrating individual and social creativity. International Journal of Human-Computer Studies, 63(4–5), 482–512. doi:10.1016/j.ijhcs.2005.04.014
  • Florida, R. (2014). The rise of the creative class, revisited. NewYork, NY: Hachette.
  • Fujita, M. (2018). AI and the future of the brain power society: When the descendants of Athena and Prometheus work together. Review of International Economics, 26(3), 508–523. doi:10.1111/roie.12310
  • Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA. (pp. 2414–2423). doi:10.1109/CVPR.2016.265
  • Gioti, A. (2020). From artificial to extended intelligence in music composition. Organised Sound, 25(1), 25–32. doi:10.1017/S1355771819000438
  • Glaveanu, V. P., & Kaufman, J. C. (2019). A historical perspective. In J. Kaufman R. & Sternberg (Eds.). Cambridge, UK: The Cambridge Handbook of Creativity (2nd ed.). Cambridge University Press. doi:10.1017/9781316979839.
  • Glaveanu, V., Lubart, T., Bonnardel, N., Botella, M., de Biaisi, P., Desainte-Catherine, M., … Mouchiroud, C. (2013). Creativity as action: Findings from five creative domains. Frontiers in Psychology, 4, 1–14. doi:10.3389/fpsyg.2013.00176
  • Grant, J. (2018). Creativity as an artistic merit. In B. Gaut & M. Kieran (Eds.), Creativity and philosophy (pp. 333–349). Abingdon, UK: Routledge.
  • Guilford, J. P. (1950). Creativity. American Psychologist, 5(9), 444–454. doi:10.1037/h0063487
  • Hawkins, J. (2021). A thousand brains - a new theory of intelligence. New York, NY: Basic Books.
  • Hautala, J. (2021). Can robots possess knowledge? Rethinking the DIK (W) pyramid through the lens of employees of an automotive factory. Humanities and Social Sciences Communications, 8(1), 1–10. doi:10.1057/s41599-021-00893-9
  • Hautala, J., & Ibert, O. (2018). Creativity in arts and sciences: Collective processes from a spatial perspective. Environment and Planning A, 50(8), 1688–1696. doi:10.1177/0308518X18786967
  • Hautala, J., & Jauhiainen, J. S. (2014). Spatio-temporal processes of knowledge creation. Research Policy, 43(4), 655–668. doi:10.1016/j.respol.2014.01.002
  • Hautala, J., & Jauhiainen, J. S. (2019). Creativity-related mobilities of peripheral artists and scientists. GeoJournal, 84(2), 381–394. doi:10.1007/s10708-018-9866-3
  • Hautala, J., & Jauhiainen, J. S. (2022). Co-creating Knowledge with Robots: System, Synthesis, and Symbiosis. Journal of the Knowledge Economy, 1–21.
  • Hayles, N. K. (2017). Unthought: The power of the cognitive nonconscious. University of Chicago Press. doi:10.7208/chicago/9780226447919.001.0001
  • Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18. doi:10.3390/arts7020018
  • Hong, J., Curran, N. M., Harandi, M., Zhou, Z., Pietikäinen, M., & Zhao, G. (2019). Artificial intelligence, artists, and art: Attitudes toward artwork produced by humans vs. artificial intelligence. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 15(2s), 1–16. doi:10.1145/3326337
  • Hsieh, H., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. doi:10.1177/1049732305276687
  • Huizinga, J. (1938/1955). Homo ludens: A study of the play element in culture. Boston, MA: Beacon Press.
  • Ihde, D. (1990). Technology and the lifeworld. Bloomington, IN: Indiana University Press.
  • Jeon, M., Fiebrink, R., Edmonds, E. A., & Herath, D. (2019). From rituals to magic: Interactive art and HCI of the past, present, and future. International Journal of Human-Computer Studies, 131, 108–119. doi:10.1016/j.ijhcs.2019.06.005
  • Jones, M. (2009). Phase space: Geography, relational thinking, and beyond. Progress in Human Geography, 33(4), 487–506. doi:10.1177/0309132508101599
  • Jordanous, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28(2), 194–216. doi:10.1080/09540091.2016.1151860
  • Kantosalo, A., & Riihiaho, S. (2019). Experience evaluations for human–computer co-creative processes–planning and conducting an evaluation in practice. Connection Science, 31(1), 60–81. doi:10.1080/09540091.2018.1432566
  • Kantosalo, A., & Toivonen, H. (2016). Modes for creative human-computer collaboration: Alternating and task-divided co-creativity. In A. Cardoso, V. Corruble, & F. Ghedini (Eds.), Proceedings of the Seventh International Conference on Computational Creativity (pp. 77–84). Paris, France.
  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. doi:10.1016/j.bushor.2018.08.004
  • Karimi, P., Rezwana, J., Siddiqui, S., Maher, M. L., & Dehbozorgi, N. (2020). Creative sketching partner: An analysis of human-AI co-creativity. In Proceedings of the 25th International Conference on Intelligent User Interfaces, 221–230. Cagliary, Italy. doi:10.1145/3377325.3377522
  • Kaufman, J. C. (2012). Counting the muses: Development of the Kaufman domains of creativity scale (K-DOCS). Psychology of Aesthetics, Creativity, and the Arts, 6(4), 298. doi:10.1037/a0029751
  • Kirsch, C., Lubart, T., & Houssemand, C. (2014). Creative personality: The importance of an autonomous working style. In E. Xeni (Ed.), Creativity in educational research and practice (pp. 123–140). Brill. doi:10.1163/9781848883086_012
  • Klausen, S. H. (2014). Interdisciplinarity and creativity. In E. Shiu (Ed.), Creativity research. an interdisciplinary and multidisciplinary research handbook (pp. 31–50). London: Routledge.
  • Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). Thousand Oaks, CA: Sage publications.
  • Lundman, R. (2012). Kaupunki leikkikenttänä [City as a Playground]. Alue Ja Ympäristö, 41(1), 3–13.
  • Latour, B. (2013). Reassembling the social. An introduction to actor-network-theory. Journal of Economic Sociology, 14(2), 73–87. doi:10.17323/1726-3247-2013-2-73-87
  • Lee, J., & Hollister, J. M. (2020). Internet-mediated research in the age of social distancing: Methodological reflections and recommendations from two online research projects. Journal of Korean Library and Information Science Society, 51(2), 319–353. doi:10.16981/kliss.51.2.202006.319
  • Lin, Y., Guo, J., Chen, Y., Yao, C., & Ying, F. (2020). It is your turn: Collaborative ideation with a co-creative robot through sketch. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY: Association for Computing Machinery.
  • Lubart, T. I. (2001). Models of the creative process: Past, present and future. Creativity Research Journal, 13(3–4), 295–308. doi:10.1207/S15326934CRJ1334_07
  • Lubart, T., & Guignard, J. (2004). The generality-specificity of creativity: A multivariate approach. In R. J. Sternberg, E. L. Grigorenko, & J. L. Singer (Eds.), Creativity: From potential to realization (p. 43–56). Washington, DC: American Psychological Association. doi:10.1037/10692-004
  • Lucas, B. (2001). Creative teaching, teaching creativity and creative learning. In A. Craft, A. Craft, & A. Craft (Eds.), Creativity in education (pp. 35–44). Continuum.
  • Maher, M. L. (2012). Computational and collective creativity: Who’s being creative? In M. L. Maher, K. Hammond, A. Pease, R. Pérez y Pérez, D. Ventura & G. Wiggins (Eds.), Proceedings of the Third International Conference on Computational Creativity (pp. 67–71). Dublin, Ireland: University College Dublin.
  • Marnin-Distelfeld, S., & Dorchin, U. (2020). “I am not an artist, I make art”: Amateurish artists in Israel and the sense of creativity. Creativity Studies, 13(1), 64–86. doi:10.3846/cs.2020.9907
  • Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. doi:10.3390/arts8010026
  • Meusburger, P., Funke, J., & Wunder, E. (2009). Introduction: The spatiality of creativity. In P. Meusburger, J. Funke, & E. Wunder (Eds.), Milieus of creativity (pp. 1–10). Springer. doi:10.1007/978-1-4020-9877-2_1
  • Miller, A. I. (2019). The Artist in the Machine: The World of AIPowered Creativity. Cambride, MA: Mit Press.
  • Miller, A. I. (2020). Creativity in the Age of AI: Computers and artificial neural networks are redefining the relationship between art and science. American Scientist, 108(4), 244–249. doi:10.1511/2020.108.4.244
  • Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. New York, NY: Penguin UK.
  • Müller, V. C. (2021). Ethics of artificial intelligence and robotics. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Accessed 1 March 2022 https://plato.stanford.edu/archives/sum2021/entries/ethics-ai
  • Nieves, J., & Osorio, J. (2013). The role of social networks in knowledge creation. Knowledge Management Research & Practice, 11(1), 62–77. doi:10.1057/kmrp.2012.28
  • Pease, A., & Colton, S. (2011). On impact and evaluation in computational creativity: A discussion of the Turing Test and an alternative proposal. In D. Kazakov & G. Tsoulas (Eds.), Proceedings of AISB ‘11: computing and philosophy (pp. 15–22). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
  • Pereira, F. C. (2007). Creativity and artificial intelligence: A conceptual blending approach. Berlin, Germany: Walter de Gruyter.
  • Piplica, A., DeLeon, C., & Magerko, B. (2012). Full-Body gesture interaction with improvisational narrative agents. In Y. Nakano, M. Neff, A. Paiva, & M. Walker (Eds.), Intelligent virtual agents 2012. Lecture notes in computer science (Vol. 7502, pp. 514–516). Springer. doi:10.1007/978-3-642-33197-8_63
  • Puryear, J. S., & Lamb, K. N. (2020). Defining creativity: How far have we come since plucker, beghetto, and dow? Creativity Research Journal, 32(3), 206–214. doi:10.1080/10400419.2020.1821552
  • Ragot, M., Martin, N., & Cojean, S. (2020). Ai-generated vs. human artworks. a perception bias towards artificial intelligence? In Extended abstracts of the 2020 CHI conference on human factors in computing systems (pp. 1–10). New York, NY.
  • Rhodes, M. (1961). An analysis of creativity. The Phi Delta Kappan, 42(7), 305–310.
  • Ritchie, G. (2007). Some empirical criteria for attributing creativity to a computer program. Minds and Machines, 17(1), 67–99. doi:10.1007/s11023-007-9066-2
  • Root-Bernstein, M. (2014). Inventing imaginary worlds: From childhood play to adult creativity across the arts and sciences. Lanham, MD: Rowman & Littlefield.
  • Root-Bernstein, R., & Root-Bernstein, M. (2004). Artistic scientists and scientific artists: The link between polymathy and creativity. In R. J. Sternberg, E. L. Grigorenko, & J. L. Singer (Eds.), Creativity: From potential to realization (p. 127–151). Washington, DC: American Psychological Association. doi:10.1037/10692-008
  • Rose, R. (2017). Posthuman agency in the digitally mediated city: Exteriorization, individuation, reinvention. Annals of the American Association of Geographers, 107(4), 779–793. doi:10.1080/24694452.2016.1270195
  • Roudavski, S., & McCormack, J. (2016). Post-anthropocentric creativity. Digital Creativity, 27(1), 3–6. doi:10.1080/14626268.2016.1151442
  • Runco, M. A. (1994). Problem finding, problem solving, and creativity. Norwood, NJ: Greenwood Publishing Group.
  • Runco, M. A., & Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creativity Research Journal, 24(1), 66–75. doi:10.1080/10400419.2012.652929
  • Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. doi:10.1080/10400419.2012.650092
  • Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Harlow, UK: Pearson.
  • Salen, K., & Zimmerman, E. (2003). Rules of play: Game design fundamentals. Cambridge, MA: MIT Press.
  • Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G., Monje-Paulson, L., & Zimmerman, H. B. (2017). Anatomy of an enduring gender gap: The evolution of women’s participation in computer science. The Journal of Higher Education, 88(2), 258–293. doi:10.1080/00221546.2016.1257306
  • Simonton, D. K. (2004). Creativity in science: Chance, logic, genius, and zeitgeist. Cambridge, UK: Cambridge University Press.
  • Simonton, D. K. (2013). What is a creative idea? little-c versus big-C creativity. In J. Chan & K. Thomas (Eds.), Handbook of Research on Creativity. Cheltenham, UK: Edward Elgar. doi:10.4337/9780857939814.00015
  • Snow, C. P. (1993). The two cultures (revised edition ed.). Cambridge, UK: Cambridge University Press.
  • Stein, M. I. (1987). Creativity research at the crossroads: A 1985 perspective. In S. G. Isaksen (Ed.), Frontiers of creativity research: Beyond the basics (pp. 417–427). Buffalo, NY: Bearly Ltd.
  • Sternberg, R. J. (2009). Domain-generality versus domain-specificity of creativity. In P. Meusburger, J. Funke, & E. Wunder (Eds.), Milieus of creativity (pp. 25–38). Spinger.
  • Sternberg, R. J. (2018a). A triangular theory of creativity. Psychology of Aesthetics, Creativity, and the Arts, 12(1), 50. doi:10.1037/aca0000095
  • Sternberg, R. J. (2018b). Evaluating merit among scientists. Journal of Applied Research in Memory and Cognition, 7(2), 209–216. doi:10.1016/j.jarmac.2018.03.003
  • Stevens, C. E., Jr, & Zabelina, D. L. (2020). Classifying creativity: Applying machine learning techniques to divergent thinking EEG data. NeuroImage, 219, 116990. doi:10.1016/j.neuroimage.2020.116990
  • Su, Z., Togay, G., & Côté, A. M. (2021). Artificial intelligence: A destructive and yet creative force in the skilled labour market. Human Resource Development International, 24(3), 341–352. doi:10.1080/13678868.2020.1818513
  • Toivonen, H., & Gross, O. (2015). Data mining and machine learning in computational creativity. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 265–275.
  • Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. Hoboken, NJ: John Wiley & Sons.
  • Wyse, L. (2019). Mechanisms of artistic creativity in deep learning neural networks. In K. Grace, M. Cook, D. Ventura, M. L. Maher (Eds.), Proceedings of the Tenth International Conference on Computational Creativity (ICCC’19) (pp. 116–123). Charlotte, UK: Association for Computational Creativity.
  • Zylinska, J. (2020). AI art. London, UK: Open Humanities Press.