R/detectors.R

In modeldatatoo: More Data Sets Useful for Modeling Examples

#' Predictions from GPT Detectors
#'
#' @description
#'
#' Data derived from the paper _GPT detectors are biased against
#' non-native English writers._ The study authors carried out a series of
#' experiments passing a number of essays to different GPT detection models.
#' Juxtaposing detector predictions for papers written by native and non-native
#' English writers, the authors argue that GPT detectors disproportionately
#' classify real writing from non-native English writers as AI-generated.
#'
#' @param ... Arguments passed to [pins::pin_read()].
#' @return tibble
#'
#' @details
#'
#' A data frame with 6,185 rows and 9 columns:
#' \describe{
#'   \item{kind}{Whether the essay was written by a `"Human"` or `"AI"`.}
#'   \item{.pred_AI}{The class probability from the GPT detector that the
#'     inputted text was written by AI.}
#'   \item{.pred_class}{The uncalibrated class prediction, encoded as
#'     `if_else(.pred_AI > .5, "AI", "Human")`}
#'   \item{detector}{The name of the detector used to generate the predictions.}
#'   \item{native}{For essays written by humans, whether the essay was written
#'     by a native English writer or not. These categorizations are coarse;
#'     values of `"Yes"` may actually be written by people who do not write with
#'     English natively. `NA` indicates that the text was not written by a human.}
#'   \item{name}{A label for the experiment that the predictions were generated
#'     from.}
#'   \item{model}{For essays that were written by AI, the name of the model
#'     that generated the essay.}
#'   \item{document_id}{A unique identifier for the supplied essay. Some
#'     essays were supplied to multiple detectors. Note that some essays
#'     are AI-revised derivatives of others.}
#'   \item{prompt}{For essays that were written by AI, a descriptor for the
#'     form of "prompt engineering" passed to the model.}
#' }
#'
#' # tibble print
#'
#' ```{r}
#' data_detectors()
#' ```
#'
#' # glimpse()
#'
#' ```{r}
#' tibble::glimpse(data_detectors())
#' ```
#'
#' @source
#'
#' \url{https://simonpcouch.github.io/detectors/}
#' \doi{10.1016/j.patter.2023.100779}
#'
#' @examples
#' \donttest{
#' data_detectors()
#' }
#' @export
data_detectors <- function(...) {
  pins::pin_read(modeldatatoo_board(), "detectors", ...)
}