Post hoc explanations improve consumer responses to algorithmic decisions
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
Algorithms are capable of assisting with, or making, critical decisions in many areas of consumers’ lives. Algorithms have consistently outperformed human decision-makers in multiple domains, and the list of cases where algorithms can make superior decisions will only grow as the technology evolves. Nevertheless, many people distrust algorithmic decisions. One concern is their lack of transparency. For instance, it is often unclear how a machine learning algorithm produces a given prediction. To address the problem, organizations have started providing post-hoc explanations of the logic behind their algorithmic decisions. However, it remains unclear to what extent explanations can improve consumer attitudes and intentions. Five experiments demonstrate that algorithmic explanations can improve perceptions of transparency, attitudes, and behavioral intentions – or they can backfire, depending on the explanation method used. The most effective explanations highlight concrete and feasible steps consumers can take to positively influence their future decision outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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