Towards Inclusive Gendered Embodiment: Designing AI Agents Based on Sociological Gender Theories
Bibliographic record
Abstract
Research has demonstrated that humans tend to unconsciously genderize socially embodied software-based or robotic agents and display stereotypical biases in their interactions with these. The gendered design of AI agents, particularly voice assistants and social robots, perpetuates harmful stereotypes and exposes these agents to sexually harassing behaviour. This programming can have severe consequences, as it risks normalizing such interactions with real women. Therefore, it is imperative to develop inclusive AI designs that actively resist and disrupt gender stereotypes rather than reinforcing them. In order to do so, a survey of existing commercially available robot designs was conducted and their morphological features studied in the context of perceived gender. Machine learning was used for interpreting feature importance in design of gendered agents. Research on the gendering of agents is still at an emergent stage and suffers from several limitations: agent gender studies lack a strong theoretical basis and are treated with binary models of masculine and feminine. Through this work, we ground our research in inclusive design of agents with sociological theories on human gender and propose rationales for inclusive designs of agents that will contribute to subversion of gender stereotyping.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.002 |
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".