An experimental investigation into whether choice architecture interventions are considered ethical
Bibliographic record
Abstract
Despite their increasing use, choice architecture interventions have faced criticism for being possibly manipulative and unethical. We empirically explore how an intervention's acceptability differs by the type of intervention used, by the domain, and by the way in which its implementation and benefits are explained. We employ a 5 × 5 × 5 factorial design with three fully crossed predictor variables: domain, type of intervention, and explanation. We measure participants' acceptance of the proposed intervention, perceived threat to autonomy and freedom of choice, and belief that the intervention will be successful. We hypothesized that acceptability of the intervention and perceived threat to autonomy will change as a function of the type of intervention used, the domain in which it is implemented, and the rationale for which its use is presented. We find that acceptability of the intervention, perceived threat to autonomy, and belief that the intervention will be successful differ by the type of intervention used and by the domain in which it is implemented. The rationale for the use of the intervention appears to change acceptability of the intervention depending on the type of intervention that is being used, and the domain in which it is implemented. Exploratory analyses were conducted to investigate differences between specific levels within factors, and interactions between factors. Given the variation in acceptability across the three factors, we believe that the discourse about the ethics of choice architecture should avoid generalizations and should instead be at the level of individual interventions in a specific situation. We conclude with a discussion about areas for future research. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 14 October 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.21758666 .
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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 | 0.032 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 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".