4th Diversity, Equity, & Inclusion in HRI Workshop
Bibliographic record
Abstract
It is crucial to prioritize diversity, equity, and inclusion (DEI) in the development of AI and robotics. Neglecting these factors not only exacerbates existing discrimination and biases, but also continues perpetuating them over time. Despite global awareness, urgent action is needed within the human-robot interaction (HRI) community. This workshop aims to bridge the gap by providing a platform for sharing experiences and research insights related to identifying, addressing, and integrating DEI principles in HRI. Building upon its last few iterations, this year's workshop will actively involve participants in tackling human biases which can be transferred to the robots, aiming to mitigate inequity, recognize and minimize prejudice, and promote inclusion within the field of HRI.
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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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".