Ethics in Persuasive Technologies: A Systematic Literature Review
Bibliographic record
Abstract
Persuasive technologies, which are intended to change users' attitudes or behaviors and encourage specific actions, are widely applied across various domains.However, the fine line between persuasion and coercion raises significant ethical concerns, which current literature only superficially addresses.This paper aims to deepen the understanding of factors influencing the ethical perception of persuasive technologies through a systematic literature review of 17 journal articles.The selected studies were analyzed using content analysis to identify key ethical factors.The findings indicate that factors such as autonomy, consent, data privacy, transparency, and addictive design strategies significantly influence users' ethical perceptions across multiple application domains.Generative artificial intelligence (AI) technologies or AI agents, particularly applications like argumentative chatbots and storytelling robots, exhibit the highest number of ethical considerations.The study also notes thematic overlaps among many ethical factors, with the context and use case impacting ethical perceptions.Based on these results, this paper offers design recommendations and suggestions for the design of ethical persuasive technology applications.
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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 | 0.052 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".