Bibliographic record
Abstract
Feminized AIs designed for in-home verbal assistance are often subjected to gendered verbal abuse by their users. I survey a variety of features contributing to this phenomenon—from financial incentives for businesses to build products likely to provoke gendered abuse, to the impact of such behavior on household members—and identify a potential worry for attempts to criticize the phenomenon; while critics may be tempted to argue that engaging in gendered abuse of AI increases the chances that one will direct this abuse toward human beings, the recent history of attempts to connect video game violence to real-world aggression suggests that things may not be so simple. I turn to Confucian discussions of the role of ritualized social interactions both to better understand the roots of the problem and to investigate potential strategies for improvement, given a complex interplay between designers and device users. I argue that designers must grapple with the entrenched sexism in our society, at the expense of “smooth” and “seamless” user interfaces, in order to intentionally disrupt entrenched but harmful patterns of interaction, but that doing so is both consistent with and recommended by Confucian accounts of social rituals.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".