About this Special Issue
The impact of AI on cultural management and cultural policy cuts across multiple disciplines and creative fields, from the theorisation of the very nature of cultural producers, to the practical construction of art form and audiences. Dynamic shifts in both conceptualisation and practice are rapidly occurring, and as such our knowledge base must reckon with these changes to inform management strategies and policy interventions. This timely special issue examines Artificial Intelligence in its broadest sense and does not seek to prescribe technologies which fall within this emerging discipline. As such, we invite papers that focus on the impact these technologies have on the production and management of art and culture.
In a visual arts and heritage context we see the use of computer vision tools to provide new insights into digitized collections, and the increasing acquisition of contemporary works that have been made or are shown through a range of AI based technologies from Neural Networks to Natural Language Processing (Manovich, 2023, Ploin et al., 2023, Murphy & Villaespesa, 2020, Villaespesa and Murphy, 2021). Alongside the production of culture, these technologies are also increasingly being used to ‘manage’ culture, from analysing visitor feedback, responding to visitor queries, generating the name of an exhibition (and associated management decisions such as staffing levels, opening hours, and ticket pricing) (French & Villaespesa, 2019), there are also examples of such technologies being used to replace and, or augment security and policing at large scale events (De Cormis, 2018, Norfolk & O’Regan, 2021). As cultural producers and creative workers across the cultural and creative industries fight for proportional remuneration and discoverability on digital platforms, the challenges of Machine Learning Recommendation Systems are compounded by the threat of AI potentially replacing their labour altogether (Anderson et al., 2020, Epstein et al., 2023, Thorne, 2020, Werner, 2020). Such concerns are at the centre of the 2023, Writers Guild of America (WGA) strike, as workers seek protection from generative AI. The use of AI technologies in their broadest sense, across the production and management of culture will have consequences that intersect with broader legislation governing AI, as well as specific industry regulation.
Connected legal intervention and sector initiatives deal also with the creation of the AI tools themselves, often trained on copyrighted material, the allocation of appropriate recognition and reward for (typically non-permissioned) use of such data sets is a key area for policy action (AI Vision Statement, 2023). The management of AI tool operation, and the ethical implications of subsequent use of tools with opaque origins beget serious questions for managers as such technology becomes embedded in the fabric of cultural production (Vear & Poltronieri, 2022). Ethical considerations also pertain to inbuilt biases from training data leading to harmful repetition within future cultural productions and drive a vital line of research.
Teasing out the myriad implications of AI tool generation and application is a substantial and time critical task. The creative affordances of this technology are quickly re-shaping the social, political, and economic context of cultural management. This special issue looks to contribute to an accompanying knowledge infrastructure.
Contributions could address, but are not limited to, the following topics and questions:
• How cultural labour practices are impacted by generative AI; what threats are presented to cultural workers; and what (management and policy) mechanisms are able to respond e.g. guild or union disputes?
• How AI technologies affect audience, visitor, patron experience (including quality of experience, ability to engage with art and art form, venue management and security).
• The role of Cultural and Creative Industries to provide a space for public understanding of these technologies, critical technology discourse, and ethical forums for emerging societal shifts.
• Emerging modes of managing artists and works, that are co-produced by AI technologies (we welcome contributions across artistic disciplines).
• The impacts of AI based tools (e.g. ML, LLM, NLP) on the recommendation, performance prediction, and evaluation of cultural works with respect to their financing, creative output arrangement, and audience construction. This broad field speaks to issues from individual artistic credit and revenue concerns, to features of cultural sovereignty protection and policy interventions regarding prominence such as the EU’s AVMSD.
• The indivisibility of AI and cultural data, and attendant copyright, data ethics, and international collaboration issues. How can the work of cultural labourers be protected and rewarded, in a way that also facilitates productive innovation? What transparency practices are required to satisfy cultural goals?
• Use of AI as a stimulant or inhibitor to cultural policy goals – countering instantiation of harmful bias and improving inclusion and representation; leveraging benefits of efficient workflows for increased sustainability.
• The impact of legislation (or lack thereof) on engaging with emerging cultural practice in the context of managing, acquiring, producing, or showing work which uses AI technologies.
The Special Issue calls for cutting edge research both in terms of policy and management practice, and in the development of productive framings for these issues.
Any questions? Please email the Editorial Office.
AI Vision Statement. (2023). Equity. https://www.equity.org.uk/advice-and-support/know-your-rights/ai-toolkit/equitys-ai-vision-statement
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020). Algorithmic Effects on the Diversity of Consumption on Spotify. Proceedings of The Web Conference 2020, 2155–2165. https://doi.org/10.1145/3366423.3380281
De Cormis, R. (2018). Facial recognition: time the regulators stepped in? Biometric Technology Today, 2018(9), 9–11. https://doi.org/10.1016/S0969-4765(18)30142-5
Epstein, Z., Hertzmann, A., the Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., Mahari, R., Pentland, A. “Sandy,” Russakovsky, O., Schroeder, H., & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
French, A., & Villaespesa, E. (2019). AI, Visitor Experience, and Museum Operations:A Closer Look at the Possible. In S. Anderson, I. Bruno, H. Hethmon, S. D. Rao, E. Rodley, & R. Ropeik (Eds.), Humanizing the digital: upproceedings from the MCN 2018 Conference (pp. 101–113). Museums Computer Network.
Manovich, L. (2023, June 1). Towards ‘General Artistic Intelligence’? Art Basel. https://www.artbasel.com/stories/lev-manovich
Murphy, O., & Villaespesa, E. (2020). AI: A Museum Planning Toolkit. Goldsmiths, University of London. https://themuseumsainetwork.files.wordpress.com/2020/02/20190317_museums-and-ai-toolkit_rl_web.pdf
Murphy, O., & Villaespesa, E. (2021). Innovation, Data and Social Responsibility. In H. Eid & M. Forstrom (Eds.), Museum innovation: building more equitable, relevant and impactful museums (pp. 109–121). Routledge, Taylor & Francis Group.
Norfolk, L., & O’Regan, M. (2021). Biometric technologies at music festivals: An extended technology acceptance model. Journal of Convention & Event Tourism, 22(1), 36–60. https://doi.org/10.1080/15470148.2020.1811184
Ploin, A., Enyon, R., Hijorth, I., & Osbourne, M. A. (2023). How Machine Learning Is Changing Artistic Work. Oxford Internet Institute. https://www.oii.ox.ac.uk/wp-content/uploads/2022/03/040222-AI-and-the-Arts_FINAL.pdf
Thorne, S. (2020). Hey Siri, tell me a story: Digital storytelling and AI authorship. Convergence: The International Journal of Research into New Media Technologies, 26(4), 808–823. https://doi.org/10.1177/1354856520913866
Vear, C., & Poltronieri, F. (Eds.). (2022). The language of creative AI: practices, aesthetics and structures. Springer.
Villaespesa, E., & Murphy, O. (2021). This is not an apple! Benefits and challenges of applying computer vision to museum collections. Museum Management and Curatorship, 36(4), 362–383. https://doi.org/10.1080/09647775.2021.1873827
Werner, A. (2020). Organizing music, organizing gender: algorithmic culture and Spotify recommendations. Popular Communication, 18(1), 78–90. https://doi.org/10.1080/15405702.2020.1715980
In a visual arts and heritage context we see the use of computer vision tools to provide new insights into digitized collections, and the increasing acquisition of contemporary works that have been made or are shown through a range of AI based technologies from Neural Networks to Natural Language Processing (Manovich, 2023, Ploin et al., 2023, Murphy & Villaespesa, 2020, Villaespesa and Murphy, 2021). Alongside the production of culture, these technologies are also increasingly being used to ‘manage’ culture, from analysing visitor feedback, responding to visitor queries, generating the name of an exhibition (and associated management decisions such as staffing levels, opening hours, and ticket pricing) (French & Villaespesa, 2019), there are also examples of such technologies being used to replace and, or augment security and policing at large scale events (De Cormis, 2018, Norfolk & O’Regan, 2021). As cultural producers and creative workers across the cultural and creative industries fight for proportional remuneration and discoverability on digital platforms, the challenges of Machine Learning Recommendation Systems are compounded by the threat of AI potentially replacing their labour altogether (Anderson et al., 2020, Epstein et al., 2023, Thorne, 2020, Werner, 2020). Such concerns are at the centre of the 2023, Writers Guild of America (WGA) strike, as workers seek protection from generative AI. The use of AI technologies in their broadest sense, across the production and management of culture will have consequences that intersect with broader legislation governing AI, as well as specific industry regulation.
Connected legal intervention and sector initiatives deal also with the creation of the AI tools themselves, often trained on copyrighted material, the allocation of appropriate recognition and reward for (typically non-permissioned) use of such data sets is a key area for policy action (AI Vision Statement, 2023). The management of AI tool operation, and the ethical implications of subsequent use of tools with opaque origins beget serious questions for managers as such technology becomes embedded in the fabric of cultural production (Vear & Poltronieri, 2022). Ethical considerations also pertain to inbuilt biases from training data leading to harmful repetition within future cultural productions and drive a vital line of research.
Teasing out the myriad implications of AI tool generation and application is a substantial and time critical task. The creative affordances of this technology are quickly re-shaping the social, political, and economic context of cultural management. This special issue looks to contribute to an accompanying knowledge infrastructure.
Contributions could address, but are not limited to, the following topics and questions:
• How cultural labour practices are impacted by generative AI; what threats are presented to cultural workers; and what (management and policy) mechanisms are able to respond e.g. guild or union disputes?
• How AI technologies affect audience, visitor, patron experience (including quality of experience, ability to engage with art and art form, venue management and security).
• The role of Cultural and Creative Industries to provide a space for public understanding of these technologies, critical technology discourse, and ethical forums for emerging societal shifts.
• Emerging modes of managing artists and works, that are co-produced by AI technologies (we welcome contributions across artistic disciplines).
• The impacts of AI based tools (e.g. ML, LLM, NLP) on the recommendation, performance prediction, and evaluation of cultural works with respect to their financing, creative output arrangement, and audience construction. This broad field speaks to issues from individual artistic credit and revenue concerns, to features of cultural sovereignty protection and policy interventions regarding prominence such as the EU’s AVMSD.
• The indivisibility of AI and cultural data, and attendant copyright, data ethics, and international collaboration issues. How can the work of cultural labourers be protected and rewarded, in a way that also facilitates productive innovation? What transparency practices are required to satisfy cultural goals?
• Use of AI as a stimulant or inhibitor to cultural policy goals – countering instantiation of harmful bias and improving inclusion and representation; leveraging benefits of efficient workflows for increased sustainability.
• The impact of legislation (or lack thereof) on engaging with emerging cultural practice in the context of managing, acquiring, producing, or showing work which uses AI technologies.
The Special Issue calls for cutting edge research both in terms of policy and management practice, and in the development of productive framings for these issues.
Any questions? Please email the Editorial Office.
AI Vision Statement. (2023). Equity. https://www.equity.org.uk/advice-and-support/know-your-rights/ai-toolkit/equitys-ai-vision-statement
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020). Algorithmic Effects on the Diversity of Consumption on Spotify. Proceedings of The Web Conference 2020, 2155–2165. https://doi.org/10.1145/3366423.3380281
De Cormis, R. (2018). Facial recognition: time the regulators stepped in? Biometric Technology Today, 2018(9), 9–11. https://doi.org/10.1016/S0969-4765(18)30142-5
Epstein, Z., Hertzmann, A., the Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., Mahari, R., Pentland, A. “Sandy,” Russakovsky, O., Schroeder, H., & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
French, A., & Villaespesa, E. (2019). AI, Visitor Experience, and Museum Operations:A Closer Look at the Possible. In S. Anderson, I. Bruno, H. Hethmon, S. D. Rao, E. Rodley, & R. Ropeik (Eds.), Humanizing the digital: upproceedings from the MCN 2018 Conference (pp. 101–113). Museums Computer Network.
Manovich, L. (2023, June 1). Towards ‘General Artistic Intelligence’? Art Basel. https://www.artbasel.com/stories/lev-manovich
Murphy, O., & Villaespesa, E. (2020). AI: A Museum Planning Toolkit. Goldsmiths, University of London. https://themuseumsainetwork.files.wordpress.com/2020/02/20190317_museums-and-ai-toolkit_rl_web.pdf
Murphy, O., & Villaespesa, E. (2021). Innovation, Data and Social Responsibility. In H. Eid & M. Forstrom (Eds.), Museum innovation: building more equitable, relevant and impactful museums (pp. 109–121). Routledge, Taylor & Francis Group.
Norfolk, L., & O’Regan, M. (2021). Biometric technologies at music festivals: An extended technology acceptance model. Journal of Convention & Event Tourism, 22(1), 36–60. https://doi.org/10.1080/15470148.2020.1811184
Ploin, A., Enyon, R., Hijorth, I., & Osbourne, M. A. (2023). How Machine Learning Is Changing Artistic Work. Oxford Internet Institute. https://www.oii.ox.ac.uk/wp-content/uploads/2022/03/040222-AI-and-the-Arts_FINAL.pdf
Thorne, S. (2020). Hey Siri, tell me a story: Digital storytelling and AI authorship. Convergence: The International Journal of Research into New Media Technologies, 26(4), 808–823. https://doi.org/10.1177/1354856520913866
Vear, C., & Poltronieri, F. (Eds.). (2022). The language of creative AI: practices, aesthetics and structures. Springer.
Villaespesa, E., & Murphy, O. (2021). This is not an apple! Benefits and challenges of applying computer vision to museum collections. Museum Management and Curatorship, 36(4), 362–383. https://doi.org/10.1080/09647775.2021.1873827
Werner, A. (2020). Organizing music, organizing gender: algorithmic culture and Spotify recommendations. Popular Communication, 18(1), 78–90. https://doi.org/10.1080/15405702.2020.1715980
Keywords: Artificial Intelligence, Generative AI, Digital Policy, Copyright, Operations
