Ethical AI and Museums: Challenges and new directions

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Ethical AI and Museums: Challenges and new directions

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Author(s): Stefania Boiano , Ann Borda , Giuliano Gaia , Guido Di Fraia

Publication date (Print): July 2024

Conference name: Proceedings of EVA London 2024 (EVA 2024)

Conference theme: Since 1990, the EVA London Conference has established itself as one of the United Kingdom’s most innovative and interdisciplinary conferences in the field of digital visualisation. The papers and abstracts in this volume cover areas such as the arts, culture, heritage, museums, music, performance, visual art, and visualisation, as well as related interdisciplinary areas, in combination with technology. The latest research and work by early career researchers, established scholars, practitioners, research students, and visual artists, can be found in this volume, published in full colour.

Conference date: 8–12 July 2024

Keywords: Artificial intelligence, Creative AI, Digital heritage, Ethics, Museum collections, Machine learning

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Stefania Boiano

Ann Borda

Giuliano Gaia

Guido Di Fraia

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Publication date: July 2024

Publication date (Print): July 2024

Pages: 18-25

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[0001]InvisibleStudio Ltd

London, UK

[0002]The Alan Turing Institute and University College London, UK

[0003]IULM University

Milan, Italy

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DOI: 10.14236/ewic/EVA2024.4

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Conference name: Proceedings of EVA London 2024

Conference acronym: EVA 2024

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Conference location: London

Conference date: 8–12 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

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1477-9358 BCS Learning & Development

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REFERENCES

Abbattista, F., Bordoni, L. & Semeraro G. (2003) Artificial intelligence for cultural heritage and digital libraries, Applied Artificial Intelligence, 17:8-9, 681-686, DOI: 10.1080/713827258
Assael, Y., Sommerschield, T., Shillingford, B. et al. (2022). Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283. https://doi.org/10.1038/s41586-022-04448-z
Beelen, K., Hosseini, K., McDonough, K., Wilson, D. (2021). MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale. arXiv:2111.15592, arXiv, 30 Nov. 2021.
Boiano S., Caldarini M., Gaia G. (2003) Make Your Museum Talk: Natural Language Interfaces For Cultural Institutions, Museums and the Web Proceedings https://www.archimuse.com/mw2003/papers/gaia/gaia.html
Boiano S., Borda, A., Gaia, G. (2019) Participatory innovation and prototyping in the cultural sector: A case study. In: Weinel, J., Bowen, J.P., Diprose, G., Lambert, N. (eds) (2019) EVA London 2019: Electronic Visualisation and the Arts: pp. 18–26. DOI: 10.14236/ewic/EVA2019.3
Borda, A., Bowen, JP. (2020) Turing’s Sunflowers: Public research and the role of museums. In: Weinel, J., Bowen, J.P., Diprose, G., Lambert, N. (eds) (2020) EVA London 2020: Electronic Visualisation and the Arts, pp. 32–39. DOI: 10.14236/ewic/EVA2020.5
Burr, C. and Leslie, D. (2022). Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. AI and Ethics, June 2022. doi:10.1007/s43681-022-00178-0.
Ceccaroni, L., et al. (2023). Advancing the productivity of science with citizen science and artificial intelligence. in Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research, OECD Publishing, Paris, https://doi.org/10.1787/69563b12-en.
Cooper, C. B., Rasmussen, L.M. and Jones, E.D. (2022) A Toolkit for Data Ethics in the Participatory Sciences. Citizen Science Association. Available at: www.citizenscience.org/data-ethics
Dikow, R.B., DiPietro, C., Trizna, M.G., BredenbeckCorp, H., Bursell, M.G., Ekwealor, J.T.B., Hodel, R.G.J., Lopez, N., Mattingly, W.J.B., Munro, J., Naples, R.M., Oubre, C., Robarge, D., Snyder, S., Spillane, JL., Tomerlin, M.J., Villanueva, L.J., White, A.E. (2023) Developing responsible AI practices at the Smithsonian Institution. Research Ideas and Outcomes 9: e113334. https://doi.org/10.3897/rio.9.e113334
Donadio M., et al. (2022) Engaging Museum Visitors with Gamification of Body and Facial Expressions. In MM '22: Proceedings of the 30th ACM International Conference on Multimedia October 2022, 7000–7002. https://doi.org/10.1145/3503161.3547744
European Parliament. (2021). Artificial intelligence in education, culture and the audiovisual sector. Brussels. A9-0127/2021 https://www.europarl.europa.eu/doceo/document/TA-9-2021-0238_EN.html
Gaia, G., Boiano, S., Borda, A. (2019) Engaging Museum Visitors with AI: The Case of Chatbots. In: Giannini T., Bowen J. (eds) Museums and Digital Culture. Springer Series on Cultural Computing, pp 309-29. Springer, Cham. https://doi.org/10.1007/978-3-319-97457-6_15
Greshake Tzovaras, B., and Ball, M. P. (2019). Alternative personal data governance models. MetaArxiv https://doi.org/10.31222/osf.io/bthj7
Hassija, V., Chamola, V., Mahapatra, A. et al. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cogn Comput 16, 45–74. https://doi.org/10.1007/s12559-023-10179-8
Huang, S., & Siddarth, D. (2023). Generative AI and the digital commons. arXiv preprint arXiv:2303.11074.
Kelley, P.G. and Woodruff, A. (2023). Advancing Explainability Through AI Literacy and Design Resources. Interactions 30, 5:, 34–38. https://doi.org/10.1145/3613249
Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. London: The Alan Turing Institute. https://doi.org/10.5281/zenodo.3240529
Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, Wong, J., and Kherroubi Garcia, I. (2023a). AI Ethics and Governance in Practice: An Introduction. The Alan Turing Institute.
Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, Wong, J., and Kherroubi Garcia, I. (2023b). AI Sustainability in Practice. Part One: Foundations for Sustainable AI Projects. The Alan Turing Institute.
Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, C., Wong, J., and Kherroubi Garcia, I. (2023c). AI Fairness in Practice. The Alan Turing Institute.
Merrit, E. (2023). Curatorial Chatbot: An Experiment with AI at the Nasher Museum of Art. AAM Center for the Future Of Museums Blog Nov 28, 2023. https://www.aam-us.org/2023/11/28/curatorial-chatbot-an-experiment-with-ai-at-the-nasher-museum-of-art/
Micheli, M., Ponti, M., Craglia, M., & Berti Suman, A. (2020). Emerging models of data governance in the age of datafication. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720948087
Murphy O. and Villaespesa E. (2020). AI: A Museum Planning Toolkit. Goldsmiths University London. https://www.ukri.org/who-we-are/how-we-are-doing/research-outcomes-and-impact/ahrc/museums-artificial-intelligence-network/
Murray, M. D., Generative AI Art: Copyright Infringement and Fair Use (August 25, 2023). SSRN: https://ssrn.com/abstract=4483539 or http://dx.doi.org/10.2139/ssrn.4483539
Nafus, D. (2023). Unclearing the air: Data’s unexpected limitations for environmental advocacy. Social Studies of Science, 0(0). https://doi.org/10.1177/03063127231201169
Ridge, M., Ferriter, M., & Blickhan, S. (2023). Recommendations, Challenges and Opportunities for the Future of Crowdsourcing in Cultural Heritage: a White Paper. Digital Scholarship at the British Library. https://doi.org/10.21428/a5d7554f.2a84f94b
Segal N. (2023). The Artist Preserving Histories with AI | The Guggenheim Museums and Foundation. Available at: https://www.guggenheim.org/articles/checklist/the-artist-preserving-histories-with-ai
Shan, S., et al. (2023). Prompt-specific poisoning attacks on text-to-image generative models. arXiv preprint arXiv:2310.13828.
Styx L (2023) How are museums using artificial intelligence, and is AI the future of museums? Museum Next 18 June 2023. https://www.museumnext.com/article/artificial-intelligence-and-the-future-of-museums/ Accessed 14 December 2023.
Toupin, S. 2024. Shaping feminist artificial intelligence. New Media & Society, 26(1), 580-595. https://doi.org/10.1177/14614448221150776
Turing, AM (1950). Computing machinery and intelligence. Mind. New Series, 59(236). (Oct. 1950), 433-460.
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Office of the High Commissioner for Human Rights (OHCHR), United Nations, 23 November 2021. Available online: https://www.ohchr.org/sites/default/files/2022-03/UNESCO.pdf (Accessed on 12 April 2023).
UNESCO (2020). UNESCO Report: Museums around the world in the face of COVID-19. https://unesdoc.unesco.org/ark:/48223/pf0000373530
Vela, D., Sharp, A., Zhang, R. et al. (2022).Temporal quality degradation in AI models. Sci Rep 12, 11654. https://doi.org/10.1038/s41598-022-15245-z
Villa, A. (2023) Getty Releases AI-Image Maker Trained on Company’s Data. Art News, 25 September 2023. Available at: https://www.artnews.com/art-news/news/getty-releases-ai-image-maker-trained-on-own-data-1234680408/
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): 362-38 DOI: http://doi.org/10.1080/09647775.2021.1873827
Zhang, H., Ahmed, FA., Fatih, D., Kitessa, A., Alhanahnah M., Leitner, P., Ali-Eldin. A. (2022). Machine Learning Systems are Bloated and Vulnerable arXiv preprint arxiv:2212.09437

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[1] Abbattista, F., Bordoni, L. & Semeraro G. (2003) Artificial intelligence for cultural heritage and digital libraries, Applied Artificial Intelligence, 17:8-9, 681-686, DOI: 10.1080/713827258

[2] Assael, Y., Sommerschield, T., Shillingford, B. et al. (2022). Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283. https://doi.org/10.1038/s41586-022-04448-z

[3] Beelen, K., Hosseini, K., McDonough, K., Wilson, D. (2021). MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale. arXiv:2111.15592, arXiv, 30 Nov. 2021.

[4] Boiano S., Caldarini M., Gaia G. (2003) Make Your Museum Talk: Natural Language Interfaces For Cultural Institutions, Museums and the Web Proceedings https://www.archimuse.com/mw2003/papers/gaia/gaia.html

[5] Boiano S., Borda, A., Gaia, G. (2019) Participatory innovation and prototyping in the cultural sector: A case study. In: Weinel, J., Bowen, J.P., Diprose, G., Lambert, N. (eds) (2019) EVA London 2019: Electronic Visualisation and the Arts: pp. 18–26. DOI: 10.14236/ewic/EVA2019.3

[6] Borda, A., Bowen, JP. (2020) Turing’s Sunflowers: Public research and the role of museums. In: Weinel, J., Bowen, J.P., Diprose, G., Lambert, N. (eds) (2020) EVA London 2020: Electronic Visualisation and the Arts, pp. 32–39. DOI: 10.14236/ewic/EVA2020.5

[7] Burr, C. and Leslie, D. (2022). Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. AI and Ethics, June 2022. doi:10.1007/s43681-022-00178-0.

[8] Ceccaroni, L., et al. (2023). Advancing the productivity of science with citizen science and artificial intelligence. in Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research, OECD Publishing, Paris, https://doi.org/10.1787/69563b12-en.

[9] Cooper, C. B., Rasmussen, L.M. and Jones, E.D. (2022) A Toolkit for Data Ethics in the Participatory Sciences. Citizen Science Association. Available at: www.citizenscience.org/data-ethics

[10] Dikow, R.B., DiPietro, C., Trizna, M.G., BredenbeckCorp, H., Bursell, M.G., Ekwealor, J.T.B., Hodel, R.G.J., Lopez, N., Mattingly, W.J.B., Munro, J., Naples, R.M., Oubre, C., Robarge, D., Snyder, S., Spillane, JL., Tomerlin, M.J., Villanueva, L.J., White, A.E. (2023) Developing responsible AI practices at the Smithsonian Institution. Research Ideas and Outcomes 9: e113334. https://doi.org/10.3897/rio.9.e113334

[11] Donadio M., et al. (2022) Engaging Museum Visitors with Gamification of Body and Facial Expressions. In MM '22: Proceedings of the 30th ACM International Conference on Multimedia October 2022, 7000–7002. https://doi.org/10.1145/3503161.3547744

[12] European Parliament. (2021). Artificial intelligence in education, culture and the audiovisual sector. Brussels. A9-0127/2021 https://www.europarl.europa.eu/doceo/document/TA-9-2021-0238_EN.html

[13] Gaia, G., Boiano, S., Borda, A. (2019) Engaging Museum Visitors with AI: The Case of Chatbots. In: Giannini T., Bowen J. (eds) Museums and Digital Culture. Springer Series on Cultural Computing, pp 309-29. Springer, Cham. https://doi.org/10.1007/978-3-319-97457-6_15

[14] Greshake Tzovaras, B., and Ball, M. P. (2019). Alternative personal data governance models. MetaArxiv https://doi.org/10.31222/osf.io/bthj7

[15] Hassija, V., Chamola, V., Mahapatra, A. et al. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cogn Comput 16, 45–74. https://doi.org/10.1007/s12559-023-10179-8

[16] Huang, S., & Siddarth, D. (2023). Generative AI and the digital commons. arXiv preprint arXiv:2303.11074.

[17] Kelley, P.G. and Woodruff, A. (2023). Advancing Explainability Through AI Literacy and Design Resources. Interactions 30, 5:, 34–38. https://doi.org/10.1145/3613249

[18] Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. London: The Alan Turing Institute. https://doi.org/10.5281/zenodo.3240529

[19] Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, Wong, J., and Kherroubi Garcia, I. (2023a). AI Ethics and Governance in Practice: An Introduction. The Alan Turing Institute.

[20] Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, Wong, J., and Kherroubi Garcia, I. (2023b). AI Sustainability in Practice. Part One: Foundations for Sustainable AI Projects. The Alan Turing Institute.

[21] Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, C., Wong, J., and Kherroubi Garcia, I. (2023c). AI Fairness in Practice. The Alan Turing Institute.

[22] Merrit, E. (2023). Curatorial Chatbot: An Experiment with AI at the Nasher Museum of Art. AAM Center for the Future Of Museums Blog Nov 28, 2023. https://www.aam-us.org/2023/11/28/curatorial-chatbot-an-experiment-with-ai-at-the-nasher-museum-of-art/

[23] Micheli, M., Ponti, M., Craglia, M., & Berti Suman, A. (2020). Emerging models of data governance in the age of datafication. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720948087

[24] Murphy O. and Villaespesa E. (2020). AI: A Museum Planning Toolkit. Goldsmiths University London. https://www.ukri.org/who-we-are/how-we-are-doing/research-outcomes-and-impact/ahrc/museums-artificial-intelligence-network/

[25] Murray, M. D., Generative AI Art: Copyright Infringement and Fair Use (August 25, 2023). SSRN: https://ssrn.com/abstract=4483539 or http://dx.doi.org/10.2139/ssrn.4483539

[26] Nafus, D. (2023). Unclearing the air: Data’s unexpected limitations for environmental advocacy. Social Studies of Science, 0(0). https://doi.org/10.1177/03063127231201169

[27] Ridge, M., Ferriter, M., & Blickhan, S. (2023). Recommendations, Challenges and Opportunities for the Future of Crowdsourcing in Cultural Heritage: a White Paper. Digital Scholarship at the British Library. https://doi.org/10.21428/a5d7554f.2a84f94b

[28] Segal N. (2023). The Artist Preserving Histories with AI | The Guggenheim Museums and Foundation. Available at: https://www.guggenheim.org/articles/checklist/the-artist-preserving-histories-with-ai

[29] Shan, S., et al. (2023). Prompt-specific poisoning attacks on text-to-image generative models. arXiv preprint arXiv:2310.13828.

[30] Styx L (2023) How are museums using artificial intelligence, and is AI the future of museums? Museum Next 18 June 2023. https://www.museumnext.com/article/artificial-intelligence-and-the-future-of-museums/ Accessed 14 December 2023.

[31] Toupin, S. 2024. Shaping feminist artificial intelligence. New Media & Society, 26(1), 580-595. https://doi.org/10.1177/14614448221150776

[32] Turing, AM (1950). Computing machinery and intelligence. Mind. New Series, 59(236). (Oct. 1950), 433-460.

[33] UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Office of the High Commissioner for Human Rights (OHCHR), United Nations, 23 November 2021. Available online: https://www.ohchr.org/sites/default/files/2022-03/UNESCO.pdf (Accessed on 12 April 2023).

[34] UNESCO (2020). UNESCO Report: Museums around the world in the face of COVID-19. https://unesdoc.unesco.org/ark:/48223/pf0000373530

[35] Vela, D., Sharp, A., Zhang, R. et al. (2022).Temporal quality degradation in AI models. Sci Rep 12, 11654. https://doi.org/10.1038/s41598-022-15245-z

[36] Villa, A. (2023) Getty Releases AI-Image Maker Trained on Company’s Data. Art News, 25 September 2023. Available at: https://www.artnews.com/art-news/news/getty-releases-ai-image-maker-trained-on-own-data-1234680408/

[37] 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): 362-38 DOI: http://doi.org/10.1080/09647775.2021.1873827

[38] Zhang, H., Ahmed, FA., Fatih, D., Kitessa, A., Alhanahnah M., Leitner, P., Ali-Eldin. A. (2022). Machine Learning Systems are Bloated and Vulnerable arXiv preprint arxiv:2212.09437

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Ethical AI and Museums: Challenges and new directions

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