TIM_BM_018 Theories, Methods, and Experiments in Art & AI

Faculty of Arts
Spring 2022
Extent and Intensity
1/1/0. 5 credit(s). Type of Completion: z (credit).
Teacher(s)
Emily Spratt (lecturer), doc. Mgr. Jana Horáková, Ph.D. (deputy)
Guaranteed by
doc. Mgr. Jana Horáková, Ph.D.
Department of Musicology – Faculty of Arts
Supplier department: Department of Musicology – Faculty of Arts
Timetable
Mon 16. 5. 10:00–13:40 N21, Tue 17. 5. 10:00–13:40 N21, Wed 18. 5. 10:00–13:40 N21, Thu 19. 5. 10:00–15:40 N21
Prerequisites
There are no official prerequisites except that students are fluent in English as the course will be taught in English. It is recommended, however, that students have completed some coursework in the arts, are able to fully commit their time to the intensive one-week structure of the class, and recognize that the course is reading intensive relative to its short timeframe. Overall, the course welcomes all students that have a passion for art and technology, and have interest in the history of ideas. Please note that currently the course size is limited to a maximum of 50 students in accordance with the latest Covid regulations.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 2/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50
fields of study / plans the course is directly associated with
Course objectives
The use of artificial intelligence—propelled by deep learning techniques—to analyze, curate, and even generate digital images is having a profound influence on visual culture, one that well exceeds Jacques Derrida’s anticipations of the effects of technology on society as he described them in Archive Fever. While regulation around emerging technologies such as AI is being formulated across the globe and with much urgency, a concept of “tech ethics” is being espoused by the leading technology companies that is imposing a simplistic moralistic framework onto corporate policies—often under the blindingly naïve rubric of “AI for Good” programs. By contrast, the aim of this intensive seminar is to foster a nuanced and critical discourse—focused on AI applications in the visual arts—that takes consideration of the points of convergence around the current emerging technology debates in media studies, art history, experimental artistic practice, data science ethics, hermeneutics, and philosophy. Recognizing that the influence that AI has on all images is radically shaping our contemporary visual culture, this course asks students to considers what is at stake for its future, as laws governing the use of AI on images are still in a formative stage. Although consensus on the relationship of art and AI remain nebulous, AI art—in in all of its radical manifestations—may well serve as a paradigm for policy makers. Beginning with reflection on Adorno’s prescient statement that technical rationality is the rationality of domination, the course will challenge both the cynicism and optimism around emerging technologies and their effect on visual culture. We will therefore question the accountability that media studies and art history have, if any, to steer the ethics debates spurred by today’s “culture industry” of digital images, and ask what the custodianship of this space entails by examining its structures of power, conveyed visually or through automated processes enabled by computer vision science. By interrogating the socio-cultural effects of the use of machine learning on images, such as algorithmic biases that lend to discrimination, or surveillance and privacy concerns in regard to facial recognition technologies, new and diverse perspectives on visual culture are investigated and actively encouraged. Although the mechanisms that enable technology to develop may be lending to the commodification and homogenization of visual culture, the seemingly democratic promises of open access, inclusivity, and diversity that big tech touts keep us captivated yet surprisingly uncritical. If the transformative role of AI on our visual culture is constituting a new type of archaeology of knowledge, how do we critically lend to its discourse through the theories, methods and experiments surrounding art and AI?
Learning outcomes
The intended outcome of the course is to provide students with a background on the theory and methods utilized to study our visual culture while also introducing the most influential contemporary perspectives on the ethics of AI-based technologies as they apply to art and images at large. With focus placed on the way in which AI is shaping art and visual culture today, the overall objective of the seminar is to cultivate critical thinking skills, a nuanced understanding of the relationship of art and technology, and a foundational background in academic approaches to visual culture, media studies, art history, and the ethics of emerging technologies.
The course is also designed to develop both students’ creative and analytical skills through informed historical, theoretical, and ethical considerations of the subject while actively encouraging diverse perspectives and voices on the examined topics. The discussion section of the course is intended to help students gain confidence and experience in bringing their perspectives to the table and to partake in group debates and conversations in an environment that is respectful and encouraging, yet also rigorous. The course is structured to foster students’ discussion skills in a constructive learning environment. After completing the course, students will be well-equipped to critically lend to the emerging art and AI discourse and, by extension, be able to partake in the ethical debates surrounding AI applications at large in our society. In this regard, for students, this course lends to professional theoretical and declarative knowledge of the subject(s), skills to apply their knowledge to the field(s), and general competencies related to analytical and creative thinking. Please note that this course is taught in English and the required reading materials are in English.
Syllabus
  • Theory and methods in art history, visual culture, media studies, applied computer vision science, art and AI, ethics of data science, philosophy, experimental art practice.
Literature
  • Martin Heidegger, “The Question Concerning Technology,” in The Question Concerning Technology, and Other Essays, Harper & Row, 1977, pp. 3–35.
  • Marshall McLuhan and Quentin Fiore, The Medium is the Massage: An Inventory of Effects, Random House, 1967, (selections).
  • Digital Exploration: Scan over the Google Code of Conduct, https://abc.xyz/investor/other/google-code-ofconduct/.
  • Legacy Russell, Glitch Feminism, A Manifesto, Verso, 2020, (Selections).
  • Digital Exploration: Briefly Examine the Public Domain Visualization Project, New York Public Library, http://publicdomain.nypl.org/pd-visualization.
  • Digital Exploration: Lev Manovich, Selfiecity Project, http://selfiecity.net.
  • Emily L. Spratt, “Creation, Curation, and Classification: Mario Klingemann and Emily L. Spratt in Conversation,” XRDS Magazine, ACM, vol. 24, no.2, 2018.
  • Digital Exploration: Mario Klingemann, Appropriate Response, http://quasimondo.com.
  • Optional to watch: Ex Machina (2014 film), Alex Garland.
  • Digital Exploration: Kate Crawford and Trevor Paglen, Training Humans, Fondazione Prada, 2020, http://www.fondazioneprada.org/project/training-humans/?lang=en.
  • Nick Bostrom, “Past Developments and Present Capabilities,” in Superintelligence: Paths, Dangers, Strategies, Oxford University Press, 2014, pp. 1–25.
  • Theodor W. Adorno and Max Horkheimer, “The Culture Industry,” in Dialectic of Enlightenment, trans. Edmund Jephcott, Stanford University Press, 2002, pp. 94–136
  • Frank Pasquale, The Black Box Society: The Secret Algorithms that Control Money and Information, Harvard University Press, 2015, (Chapters one and two).
  • Anupam Chander, “The Racist Algorithm?,” Michigan Law Review 115, no. 6, 2017, pp. 1023–1045.
  • Emily L. Spratt, Ahmed Elgammal, “Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science,” in Computer Vision: ECCV Conference Proceedings 2014, Springer Verlag, Fall 2014.
  • Digital Exploration: Microsoft Research, FATE: Fairness, Accountability, Transparency, and Ethics in AI, https://www.microsoft.com/en-us/research/theme/fate/.
  • Kate Crawford, “Halt the use of facial-recognition technology until it is regulated,” Nature 572, August 27, 2019, p. 565.
  • Watch: Fellini Forward (2021 film), Zackary Canepari, Drea Cooper, Max Niemann, https://www.imdb.com/title/tt15354380/.
  • Digital Exploration: Benjamin Anderson et al., Mnemosyne: Meanderings Through Aby Warburg’s Atlas, 2013, warburg.library.cornell.edu.
  • Michael Kearns and Aaron Roth, The Ethical Algorithm: The Science of Socially Aware Algorithm Design, Oxford University Press, 2019, pp. 1–93. (Note that this book reads quickly.)
  • Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, PublicAffairs, 2019, (Selections).
  • Digital Exploration: Joy Buolamwini, Gender Shades, MIT Media Lab, http://gendershades.org
  • Will Slauter, Who Own the News?: A History of Copyright, Stanford University Press, 2019, pp. 1–27 and 271–286.
  • Walter Benjamin, “The Work of Art in the Age of Mechanical Reproduction,” in Illuminations, ed. Hannah Arendt, Jonathan Cape, 1970, pp. 219–253.
  • Digital Exploration: Cathy O’Neil, “The Era of Blind Faith in Big Data Must End,” Ted Talk, April 2017, https://www.ted.com/talks/cathy_o_neil_the_era_of_blind_faith_in_big_data_must_end?language=en (this is only ten-minutes long).
  • Hito Steyerl, “In Defense of the Poor Image,” in The Wretched of the Screen, Sternberg Press, 2012, pp. 31–45.
  • Optional to watch: Videodrome (1983 film), David Cronenberg and Her (2013 film), Spike Jonze.
  • Edward Herman and Noam Chomsky, Manufacturing Consent: The Political Economy of Mass Media, Pantheon Books, 1988, (Selections).
  • Rosalind Krauss, “The Impulse to See,” in Vision and Visuality, ed. Hal Foster, Bay Press, 1988, pp. 51–75
  • Jacques Derrida, Archive Fever, University of Chicago Press, 1996, (Selections
  • André Malraux, Museum Without Walls (Le Musée Imaginaire), Secker & Warburg, 1967, (Selections).
  • Guy Debord, The Society of the Spectacle, Black & Red, 1970, (Selections).
  • Digital Exploration: Refik Anadol, Archive Dreaming, https://refikanadol.com/works/archive-dreaming/
  • Lev Manovich, Cultural Analytics, MIT Press, 2020, (Chapter one).
  • Griselda Pollock, “Is Feminism a trauma, a bad memory, or a virtual future?” Differences, A Journal of Feminist Cultural Studies, Duke University Press, 2016, pp. 27-61.
  • Michel Foucault, “Panopticism,” in Discipline and Punish: The Birth of the Prison, trans. Alan Sheridan, Vintage Books, 1995, pp. 195–228.
  • Clement Greenberg, “Avant-Garde and Kitsch,” The Partisan Review, 1939, pp. 34–49.
  • Digital Exploration: Mario Klingemann on Artificial Intelligence, Technology and our Future, https://www.sothebys.com/en/articles/artist-mario-klingemann-on-artificial-intelligence-art-tech-and-ourfuture. (Watch embedded link to interview with the arti
  • Stuart Russell, “How Might AI Progress in the Future,” in Human Compatible: Artificial Intelligence and the Problem of Control, Penguin, 2019, pp. 62–102.
  • Susan Sontag, “One Culture and the New Sensibility,” in Against Interpretation, Farrar, Straus and Giroux, 1966, pp. 293–304.
Teaching methods
Each class begins with a lecture by the professor and then follows with a discussion session. In addition, the course will feature the use of break-out groups in the discussion period. Students will be required to actively participate in the course and to make presentations within the class. Students are expected to do homework for the course which consists mainly of reading assignments and examining digital course materials, artworks, and collections online. As a final product of the course, students will create a theoretical design for an imaginary (or not imaginary!) art and AI project that they will critically discuss in a final essay.
Assessment methods
Given the short and intensive seminar format of this course, full attendance and active and engaged participation that demonstrates that students have done the readings are the most significant components of the evaluation. In addition, there will be one written essay required as a final evaluation marker of the course that encourages the use of both creative and critical-thinking approaches to the subject matter.
The course only has a pass/fail grading option.
Attendance is 40%, participation that demonstrates that the student has done the required reading is 40%, presentation of individual and group work in a professional communication style is 10%, and required final essay, which also demonstrates that the student has done the required reading and analysis of online digital artworks is 10%.
In order to pass the course, students must get a minimum evaluation of 65%.
Language of instruction
English
Further Comments
Study Materials
The course is taught only once.
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