Reimagining Informed Consent in Human-Robot Interaction: Introducing the RoboConsent Framework
Bibliographic record
Abstract
Informed consent is an integral process in human-robot interaction (HRI); however, current practices have been criticized for overlooking the social, psychological, and embodied complexities of interacting with robots. Social robots’ embodied, human-like design and social behavior can lead to misaligned expectations that pose risks such as deception, overtrust, poor user experience, and psychological harm for users. Moreover, robots often collect personal data in ways that are not always visible or understood by users. Typically, informed consent does not address such issues, highlighting the need for consent processes tailored to HRI. In this paper, we reimagine informed consent and introduce the RoboConsent framework, drawing from previous HRI research highlighting these issues and feminist consent models that address power imbalances and move toward a user-centered process. The framework consists of five components that ensure meaningful informed consent and six principles that guide how it can be obtained. These work in tandem to create informed consent practices that address the unique dynamics 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 | 0.134 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".