Informing Users about Data Imputation: Exploring the Design Space for Dealing With Non-Responses
Bibliographic record
Abstract
Machine learning algorithms often require quantitative ratings from users to effectively predict helpful content. When these ratings are unavailable, systems make implicit assumptions or imputations to fill in the missing information; however, users are generally kept unaware of these processes. In our work, we explore ways of informing the users about system imputations, and experiment with imputed ratings and various explanations required by users to correct imputations. We investigate these approaches through the deployment of a text messaging probe to 26 participants to help them manage psychological wellbeing. We provide quantitative results to report users' reactions to correct vs incorrect imputations and potential risks of biasing their ratings. Using semi-structured interviews with participants, we characterize the potential trade-offs regarding user autonomy, and draw insights about alternative ways of involving users in the imputation process. Our findings provide useful directions for future research on communicating system imputation and interpreting user non-responses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".