Page 90 - AC-2-3

Arts & Communication Computer vision in tactical AI art

65. Shinseungback Kimyonghun. Mind. Shinseungback 77. Yang K, Qinami K, Fei-Fei L, Deng J, Russakovsky O.
Kimyonghun’s Website; 2019. Available from: https://ssbkyh. Towards Fairer Datasets: Filtering and Balancing the
com/works/mind [Last accessed on 2024 Apr 05]. Distribution of the People Subtree in the ImageNet Hierarchy.
ImageNet website; 2019. Available from: https://image-net.
66. Lozano-Hemmer R. Level of Confidence. Rafael Lozano- org/update-sep-17-2019.php [Last accessed 2024 Apr 05].
Hemmer’s Website; 2015. Available from: https://www.
lozano-hemmer.com/level_of_confidence.php [Last 78. Lyons MJ. Excavating ‘Excavating AI’: The Elephant in the
accessed on 2024 Apr 05]. Gallery. arXiv preprint; 2020. Available from https://arxiv.
org/abs/2009.01215 [Last accessed on 2024 Apr 05].
67. Zer-Aviv M. The Normalizing Machine. Mushon Zer-Aviv’s
Website; 2018. Available from: https://mushon.com/tnm 79. Leibowicz C, Saltz E, Coleman L. Creating AI art responsibly:
[Last accessed: 2024 Apr 05]. A field guide for artists. Diseña. 2021;19:5.
68. Mitchell M. Artificial Intelligence: A Guide for Thinking doi: 10.7764/disena.19.Article.5
Humans, 88-90. Kindle edition. New York, NY: Farrar, 80. Lossin RH. Trevor Paglen’s Unstable Truths. e-flux Criticism;
Straus and Giroux; 2019. 2023. Available from: https://editor.e-flux-systems.com/
69. Kearns M, Roth A. The Ethical Algorithm: The Science of files/544198_e-flux-criticism-trevor-paglen-s-unstable-
Socially Aware Algorithm Design, 32-48. Oxford, UK: Oxford truths.pdf [Last accessed on 2024 Apr 05].
University Press; 2019. 81. Groß B, Lee J. The Aerial Bold Project. Benedikt Groß’s
70. Pasquinelli M. Abnormal Encephalization in the Age of Website; 2016. Available from https://benedikt-gross.
Machine Learning. e-flux; 2016. Available from: https:// de/projects/the-aerial-bold-project [Last accessed on
worker01.e-flux.com/pdf/article_9009069.pdf [Last 2024 Apr 05].
accessed on 2024 Apr 05]. 82. Wilner RG. Pareidolia and the pitfalls of subjective
71. Orcutt M. Are Face Recognition Systems Accurate? interpretation of ambiguous images in art history. Leonardo.
Depends on Your Race. MIT Technology Review Website; 2021;54(6):638-642.
2016. Available from: https://www.technologyreview. 83. Gradecki J, Curry D. Boogaloo Bias. Project website; 2022.
com/2016/07/06/158971/are-face-recognition-systems- Available from https://www.boogaloo-bias.art [Last accessed
accurate-depends-on-your-race [Last accessed on on 2024 Apr 05].
2024 Apr 05].
84. Larson EJ. The Myth of Artificial Intelligence: Why Computers
72. Buolamwini J, Gebru T. Gender Shades: Intersectional Can’t Think the Way We Do, 76-83. Cambridge/London, UK:
Accuracy Disparities in Commercial Gender Classification, The Belknap Press of Harvard University Press; 2021.
In: Friedler SA, Wilson C, editors. Proceedings of the
1 Conference on Fairness, Accountability and Transparency 85. Schmieg S. Decisive Camera. Sebastian Schmieg’s website;
st
81. New York; 2018. p. 77-91. Available from: https:// 2018. Available from: https://sebastianschmieg.com/
proceedings.mlr.press/v81/buolamwini18a.html [Last decisive-camera [Last accessed on 2024 Apr 05].
accessed on 2024 Apr 05]. 86. Schmieg S. Decisive Mirror. Sebastian Schmieg’s website;
73. Gershgorn D. How a 2018 Research Paper Led Amazon, 2019. Available from: https://sebastianschmieg.com/
Microsoft, and IBM to Curb Their Facial Recognition decisive-mirror [Last accessed on 2024 Apr 05].
Programs. OneZero Medium website; 2020. Available from: 87. Zeilinger M. Tactical Entanglements: AI Art, Creative Agency,
https://onezero.medium.com/how-a-2018-research-paper- and the Limits of Intellectual Property, 51-55. Lüneburg, DE:
led-to-amazon-and-ibm-curbing-their-facial-recognition- Meson Press; 2021
programs-db9d6cb8a420 [Last accessed on 2024 Apr 05].
88. Basanta A. All We’d Ever Need Is One Another. Adam Basanta’s
74. Crawford K, Paglen T. Excavating AI: The Politics of Training website; 2018. Available from: https://adambasanta.com/
Sets for Machine Learning. Project website; 2019. Available allwedeverneed [Last accessed on 2024 Apr 05].
from: https://excavating.ai [Last accessed on 2024 Apr 05].
89. Zeilinger M. Tactical Entanglements: AI Art, Creative Agency,
75. Kuesel C. An Online Image Database Will Remove 600,000 and the Limits of Intellectual Property, 94-108. Lüneburg,
Pictures After an Art Project Revealed the System’s Racist DE: Meson Press; 2021.
Bias; 2019. Available from: https://www.artsy.net/article/
artsy-editorial-online-image-database-will-remove-600– 90. Curry D. Artistic defamiliarization in the age of algorithmic
000-pictures-art-project-revealed-systems-racist-bias [Last prediction. Leonardo. 2023;56(2):177-182.
accessed on 2024 Apr 05]. 91. Smil V. Invention and Innovation: A Brief History of Hype
and Failure. Cambridge, MA: The MIT Press; 2023. p. 157-
76. Arpteg A. Reflections on the ImageNet Roulette Provocation;
2019. Available from: https://www.linkedin.com/pulse/ 165.
reflections-imagenet-roulette-provocation-anders-arpteg 92. Żylińska J. AI Art: Machine Visions and Warped Dreams,
[Last accessed on 2024 Apr 05]. 75-85. London, UK: Open Humanities Press; 2020

Volume 2 Issue 3 (2024) 17 doi: 10.36922/ac.2282