Rage Against The Machine: A Roundtable on an Arts Community’s Reactions Toward AI Art
Bibliographic record
Abstract
This is a community engagement session, with its own Zoom information: please join this link instead of the conference track - https://ubc.zoom.us/j/64078424986?pwd=5Ha5O8RQaloVCQaMIDn76P3TmeZDzE.1 Meeting ID: 640 7842 4986 Passcode: 177432 On February 1st, 2024, a non-profit DIY music and art space in Vancouver, BC, Red Gate Arts Society, posted on Instagram a poster of an upcoming show of “an evening of improvisation and remediated digital sound” by a group called Seethruzoo. This poster, immediately after it was uploaded, exploded with outraged commentary. The background image of the poster was distinctively AI generated and the arts community around this beloved venue reacted in disgust and anger— “ew ai art??? Really?” “AI ‘art’ is art theft.” “Surely one of you can source an artist to make a poster for you. There’s a large pool of talent to draw from, and folks need illustration/design gigs. AI generated images mostly look like crap anyway but that’s not the heart of the issue.” “What does this say that art again becomes the most devalued aspect of an arts society?” Some replies to these comments included a defense of the venue stating that the poster was not designed by the venue, but the artists of the event themselves, a call for a more constructive discussion space, and the society co-founder expressing her willingness to review their booking policies. The topic of this proposed roundtable is the controversy around this poster—which was made by one of the members of Seethruzoo who is a professional designer. In dialogue with the audience, the panelists of this roundtable will illuminate different aspects of AI use for artistic expressions. Questions addressed in this roundtable include: (1) Why is it that AI generated arts provoke such visceral, aversive, and moralizing responses? (2) What would be constructive ways to talk about and deal with AI innovations among the arts community? (3) What kinds of practices and policies around the use of AI would actually benefit artists? Through addressing these questions, the panelists discuss the creativity involved in generating AI images, the recognition of algorithmic Other, the differences in the intertextuality exercised by human consciousness and artificial intelligence, and so forth. As it was suggested on the comment section of the Instagram post, if ELO organizers permit us to do so, we would like to have this roundtable as an “open access” public forum and ask the Red Gate Arts Society to invite their community members to participate in the discussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".