Skip to main content

Autonomy, Authenticity, Authorship and Intention in Computer Generated Art

  • Conference paper
Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2019)


This paper examines five key questions surrounding computer generated art. Driven by the recent public auction of a work of “AI Art” we selectively summarise many decades of research and commentary around topics of autonomy, authenticity, authorship and intention in computer generated art, and use this research to answer contemporary questions often asked about art made by computers that concern these topics. We additionally reflect on whether current techniques in deep learning and Generative Adversarial Networks significantly change the answers provided by many decades of prior research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. 1.

    The name is derived from the French interpretation of “Goodfellow”: Bel ami.

  2. 2.

    Ada Lovelace is famously known as one of the first people to record ideas about computer creativity [6].

  3. 3.

    A great resource is the EvoMUSART Index, available at:

  4. 4.

    This is conceptually similar to co-evolutionary strategies, such as creator/critic systems [24], used at least since the 1990s.

  5. 5.

    It is worth noting that artistic styles or idioms share similar characteristics too.

  6. 6.


  1. Is artificial intelligence set to become art’s next medium? November 2018. Accessed 07 Nov 2018

  2. Obvious: Obvious, explained, February 2018. Accessed 01 Nov 2018

  3. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  4. Rea, N.: Has Artificial Intelligence Brought Us the Next Great Art Movement? Here Are 9 Pioneering Artists Who Are Exploring AI’s Creative Potential, November 2018. Accessed 07 Nov 2018

  5. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint, arXiv:1511.06434 (2015)

  6. Menabrea, L.F.: Sketch of the analytical engine invented by Charles Babbage, No. 82, Bibliothèque Universelle de Genève, October 1842.

  7. Burnham, J.: Systems esthetics. Artforum 7, 30–35 (1968)

    Google Scholar 

  8. Burnham, J.: On the future of art. In: Fry, E.F. (ed.) The Aesthetics of Intelligent Systems, p. 119. The Viking Press, New York (1969)

    Google Scholar 

  9. Reichardt, J.: The Computer in Art. Studio Vista; Van Nostrand Reinhold, London, New York (1971)

    Google Scholar 

  10. Davis, D.: Art and the Future. Praeger, New York (1973)

    Google Scholar 

  11. Leavitt, R.: Artist and Computer. Harmony Books, New York (1976)

    Google Scholar 

  12. Dietrich, F., Tanner, P.: Artists interfacing with technology: basic concepts of digital creation, July 1983

    Google Scholar 

  13. McCorduck, P.: Aaron’s Code: Meta-art, Artificial Intelligence and the Work of Harold Cohen. W.H. Freeman, New York (1990)

    Google Scholar 

  14. O’Hear, A.: Art and technology: an old tension. R. Inst. Philos. Suppl. 38, 143–158 (1995)

    Article  Google Scholar 

  15. Cohen, H.: The further exploits of Aaron, painter. Stanford Humanit. Rev. 4, 141–158 (1995).

    Google Scholar 

  16. Bown, O., McCormack, J.: Creative agency: a clearer goal for artificial life in the arts. In: Kampis, G., Karsai, I., Szathmáry, E. (eds.) ECAL 2009. LNCS (LNAI), vol. 5778, pp. 254–261. Springer, Heidelberg (2011).

    Chapter  Google Scholar 

  17. Boden, M.A.: Creativity and Art: Three Roads to Surprise. Oxford University Press, Oxford (2010)

    Google Scholar 

  18. McCormack, J.: Aesthetics, art, evolution. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 1–12. Springer, Heidelberg (2013).

    Chapter  Google Scholar 

  19. McCormack, J., Bown, O., Dorin, A., McCabe, J., Monro, G., Whitelaw, M.: Ten questions concerning generative computer art. Leonardo 47(2), 135–141 (2014)

    Article  Google Scholar 

  20. d’Inverno, M., McCormack, J.: Heroic vs collaborative AI for the arts. In: Yang, Q., Wooldridge, M. (eds.) Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 2438–2444. AAAI Press (2015)

    Google Scholar 

  21. Thornton, S.: Seven Days in the Art World. Granta, London (2009)

    Google Scholar 

  22. Dewey, J.: Art as Experience. Capricorn Books, New York (1934)

    Google Scholar 

  23. McCormack, J.: Working with generative systems: an artistic perspective. In: Bowen, J., Lambert, N., Diprose, G. (eds.) Electronic Visualisation and the Arts (EVA 2017), pp. 213–218. Electronic Workshops in Computing (eWiC), BCS Learning and Development Ltd., London, July 2017

    Google Scholar 

  24. Todd, P.M., Werner, G.M.: Frankensteinian methods for evolutionary music composition. In: Griffith, N., Todd, P.M. (eds.) Musical Networks: Parallel Distributed Perception and Performance, pp. 313–339. The MIT Press/Bradford Books, Cambridge (1999)

    Google Scholar 

  25. Beier, T., Neely, S.: Feature-based image metamorphosis. Comput. Graph. 26, 35–42 (1992)

    Article  Google Scholar 

  26. Dodson, C.H., Frymire, G.P.: Natural Pollination of Orchids. Missouri Botanical Garden, St. Louis (1961)

    Google Scholar 

  27. Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: ArtGAN: artwork synthesis with conditional categorical GANs. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3760–3764. IEEE (2017)

    Google Scholar 

  28. He, B., Gao, F., Ma, D., Shi, B., Duan, L.Y.: ChipGAN: a generative adversarial network for Chinese ink wash painting style transfer. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 1172–1180. ACM (2018)

    Google Scholar 

  29. Zhang, H., Dana, K.: Multi-style generative network for real-time transfer. Preprint, arXiv:1703.06953 (2017)

  30. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: creative adversarial networks, generating “art” by learning about styles and deviating from style norms. Preprint, arXiv:1706.07068 (2017)

  31. Keane, A.J., Brown, S.M.: The design of a satellite boom with enhanced vibration performance using genetic algorithm techniques. In: Parmee, I.C. (ed.) Conference on Adaptive Computing in Engineering Design and Control 96, pp. 107–113. P.E.D.C. (1996)

    Google Scholar 

  32. Hobbes, T.: Leviathan. In: Molesworth, W. (ed.) Collected English Works of Thomas Hobbes. Routledge, London (1651/1997)

    Google Scholar 

  33. Jaszi, P.: Toward a theory of copyright: the metamorphoses of authorship. Duke Law J. 40(2), 455 (1991)

    Article  Google Scholar 

  34. Veale, T.: Scoffing at mere generation (2015). Accessed 18 Aug 2015

  35. Eigenfeldt, A., Bown, O., Brown, A.R., Gifford, T.: Flexible generation of musical form: beyond mere generation. In: Proceedings of the Seventh International Conference on Computational Creativity, pp. 264–271 (2016)

    Google Scholar 

  36. Wu, A.J.: From video games to artificial intelligence: assigning copyright ownership to works generated by increasingly sophisticated computer programs. AIPLA QJ 25, 131 (1997)

    Google Scholar 

  37. Chamberlain, W.: Copyright catalog: the policeman’s beard is half constructed: computer prose and poetry. Accessed 08 Nov 2018

  38. Abbott, R.: Artificial Intelligence, Big Data and Intellectual Property: Protecting Computer-Generated Works in the United Kingdom. Edward Elgar Publishing Ltd., Cheltenham (2017)

    Google Scholar 

  39. Vincent, J.: How three French students used borrowed code to put the first AI portrait in Christie’s (2018). Accessed 08 Nov 2018

  40. Parikka, J.: Leonardo book review: the art of artificial evolution: a handbook on evolutionary art and music. Accessed 08 Feb 2019

  41. Manikonda, L., Kambhampati, S.: Tweeting AI: perceptions of lay versus expert twitterati. In: Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018), pp. 652–655 (2018)

    Google Scholar 

  42. Gaines-Ross, L.: What do people - not techies, not companies - think about artificial intelligence? Harvard Bus. Rev. (2016).

  43. Dennett, D.C.: The Intentional Stance. MIT Press, Cambridge (1987)

    Google Scholar 

  44. Sheridan, S.L.: Generative systems versus copy art: a clarification of terms and ideas. Leonardo 16(2), 103–108 (1983)

    Article  Google Scholar 

Download references


This research was support by Australian Research Council grants DP160100166 and FT170100033.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jon McCormack .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Cite this paper

McCormack, J., Gifford, T., Hutchings, P. (2019). Autonomy, Authenticity, Authorship and Intention in Computer Generated Art. In: Ekárt, A., Liapis, A., Castro Pena, M.L. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2019. Lecture Notes in Computer Science(), vol 11453. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16666-3

  • Online ISBN: 978-3-030-16667-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics