The Impact of Human Implication for AI-Supported Decisions over Perception of Trust, Agency and Dignity
Bibliographic record
Abstract
Recent developments in artificial intelligence (AI), more specifically in generative AI, are disrupting our life. The integration of generative AI raises questions pertaining not only to the performance and accuracy of the AI system, but also to the boundaries of the role of both human and AI. This calls for a better understanding of the perception of human dignity over different uses of generative AI, but also for comprehending how said perception may interact with trust into the AI and sense of agency. The goal of the current study was to evaluate the perception of human dignity, trust and sense of agency among different uses of AI-supported decisions depending on the context of use and on the level of implication of the human decision maker. We presented participants a series of vignettes where generative AI systems were used to support decision making in five domains of use (health, business, humanities, arts, and technology) and four types of support (for decision support, communication, creativity, and research). The level of human implication regarding the decision was also manipulated across two conditions. Sense of agency, trust in the AI, perception of appropriateness for the AI to make a decision, as well as interpersonal justice and dehumanization level measures were collected for each vignette. Results outlined that sense of agency differed across conditions. Domain of use influenced sense of agency, trust in the AI, decision appropriateness and dehumanization perceptions, with differences emerging mostly for health-related vignettes. The type of support also impacted trust and decision appropriateness, with more positive perceptions for vignettes discussing creativity use cases. Overall, our study sheds light on the perception of the general population over different types of AI use and how components such as perception of agency, trust and dignity may vary depending on the nature of the use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".