Assessing Trust Versus Reliance for Technology Platforms by Systematic Literature Review
Bibliographic record
Abstract
We do not trust technologies like we trust people, rather we rely on them. This article argues for an emphasis on reliance rather than trust as a concept for understanding human relationships with technology. Reliance is important because researchers can empirically measure the reliability of a given technology. We first explore two frameworks of trust and reliance. We then examine how reliance can be measured by conducting systematic literature reviews of reported success metrics for given technologies. Specifically, we examine papers which present models for predicting private traits from social media data. Of the 72 models for predicting private traits that were surveyed from 31 papers, 80% of the methods reported success rates lower than 90%, indicating a general unreliability in predicting private traits. We illustrate the current applicability of this method throughout the article by discussing the Cambridge Analytica scandal that began during the 2016 US Presidential election.
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.
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 | 0.143 | 0.495 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.079 | 0.049 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".