5,040 rated or chosen profiles from 1,008 respondents, in up to 5 tasks of 1 profile varying on 4 attributes.
One of 2 tables from the same source; this one is the trust table. The others are listed under Related tables.
| Description | US Lucid respondents (n=1,008, October 2022) each rated 5 hypothetical AI-enabled technologies varying in domain (cars, armed drones, surgery, content moderation, police surveillance), autonomy, precision and regulator, on 1-5 scales of support for and trust in the use of AI. |
|---|---|
| Reference | Kreps, S., George, J., Lushenko, P., & Rao, A. (2023). Exploring the artificial intelligence "Trust paradox": Evidence from a survey experiment in the United States. PLOS ONE, 18(7), e0288109. |
| DOI | 10.1371/journal.pone.0288109 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/EOYDJR |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,008 |
|---|---|
| Rows (one per respondent, task and profile) | 5,040 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 1 |
| Attributes | 4 |
| Topic | technology ai privacy |
|---|---|
| A profile represents | product service |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| rating_support | Do you support the use of AI under these circumstances? [rating, 1 (strongly disagree) to 5 (strongly agree)] |
|---|---|
| rating_trust | Do you trust the use of AI under these circumstances? [rating, 1 (strongly disagree) to 5 (strongly agree)] |
attr_autonomyattr_domainattr_precisionattr_regulationcov_agecov_duration_seccov_educationcov_gender_codecov_ideology_codecov_incomecov_party_id_codecov_raceidprofilerating_supportrating_trusttask
The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("kreps_2023_ai_trust", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("kreps_2023_ai_trust", source="conj")
@article{Kreps_2023, title={Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0288109}, DOI={10.1371/journal.pone.0288109}, number={7}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Kreps, Sarah and George, Julie and Lushenko, Paul and Rao, Adi}, editor={Tung, Hans H.}, year={2023}, month=July, pages={e0288109} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint v4.2 |
| Redivis dataset DOI | 10.57761/qkye-q530 |
| Manifest pin for this IRW version | v4.2 |
| Metadata source | irw_meta v44.0 |