Expecting the expected: an analytical framework to examine people’s expectations of robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a novel framework for human-robot interaction designers to analyze and explore expectations of their robot designs. It consists of a model of how people form expectations of robots, and a taxonomy for classifying them. A known challenge in human-robot interactions is expectation discrepancy, in which the expectations people form when interacting with a social robot are not aligned with its actual capabilities. This can disappoint users and hinder interaction. Research has proposed ways to mitigate expectation discrepancy, but designers lack a systematic approach to analyzing and describing expectations. We developed a rigorous theoretical framework by drawing from theories and models from psychology and sociology on expectations between people, and by conducting a field review of expectations in human-robot interactions. We further propose methods for designers to leverage the framework in systematic analysis of how and why people form expectations of a given robot and what those expectations may be. This can empower designers with greater control over people’s expectations, enabling them to combat expectation discrepancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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 it