Bibliographic record
Abstract
This paper presents a novel reflection on interaction devices between human and robots. Robots are most currently seen as tools, extensions of the human body designed and meant to serve its needs. The development of artificial intelligence forces us to reconsider that paradigm, and to ask ourselves the question of who, during the human-machine dialogue, is really in control. In the overwhelming majority of situations where robots and users are expected to collaborate or interrelate, users are required to fully trust the machine and its reactions. The authors propose a methodology for the design of interfaces that question the very core of this trust issue, through a reversed interface allowing the machine to physically control humans seen as mere peripherals. After laying down the design principles of this approach, the authors describe an art performance during which the consequences of this reversal are explored to their very limits. The radical approach of this research is made possible through the unconventional study context provided by the artistic nature of the attempt. The experimental feedback of the artist is discussed. It is followed by a survey of the audience's reactions that allows to withdraw significant conclusions from the work.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.103 | 0.011 |
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; both teacher heads agree on what is shown here.
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".