Update of the abbreviations

R/fairness_selection.R

@@ -227,7 +227,7 @@ fairness_selection <- function(q1 = NULL,
       }
     } else if (q2 == 3) {
       name <- "Equalized Odds"
-      measure <- "dp"
+      measure <- "eo"
       q2_name <- "Correct and incorrect classification"
     }
   } else if (q1 == 2) {

R/model_fairness.R

@@ -24,7 +24,7 @@
 #' quantify potential fairness or discrimination in the algorithms predictions.
 #' Available parity metrics include predictive rate parity, proportional parity,
 #' accuracy parity, false negative rate parity, false positive rate parity, true
-#' positive rate parity, negative predicted value parity, specificity parity,
+#' positive rate parity, negative predictive value parity, specificity parity,
 #' and demographic parity. The function returns an object of class
 #' \code{jfaFairness} that can be used with associated \code{summary()} and
 #' \code{plot()} methods.
@@ -98,11 +98,11 @@
 #'     \item{False positive rate parity (\code{fprp}): calculated as FP / (TN
 #'       + FP), quantifies whether the false positive rate is the same across
 #'       groups.}
-#'     \item{True positive rate parity (\code{tprp}): calculated as TP / (TP +
+#'     \item{True positive rate parity (\code{tprp}, also known as Equal opportunity): calculated as TP / (TP +
 #'       FN), quantifies whether the true positive rate is the same across
 #'       groups.}
-#'     \item{Negative predicted value parity (\code{npvp}): calculated as TN /
-#'       (TN + FN), quantifies whether the negative predicted value is equal
+#'     \item{Negative predictive value parity (\code{npvp}): calculated as TN /
+#'       (TN + FN), quantifies whether the negative predictive value is equal
 #'       across groups.}
 #'     \item{Specificity parity (\code{sp}): calculated as TN / (TN + FP),
 #'       quantifies whether the true positive rate is the same across groups.}

tests/testthat/test-fairness-selection.R

@@ -22,7 +22,7 @@ test_that(desc = "Validation of fairness selection", {
   expect_equal(outcome$measure, "pp")
   # Equalized odds
   outcome <- fairness_selection(q1 = 1, q2 = 3)
-  expect_equal(outcome$measure, "dp")
+  expect_equal(outcome$measure, "eo")
   # False negative rate parity
   outcome <- fairness_selection(q1 = 1, q2 = 2, q3 = NULL, q4 = 2)
   expect_equal(outcome$measure, "fnrp")

tests/testthat/test-model-fairness.R

@@ -53,7 +53,7 @@ test_that(desc = "Benchmark against fairness package", {
   fairness_tpr <- fairness::equal_odds(compas, outcome = "TwoYrRecidivism", preds = "Predicted", group = "Ethnicity", outcome_base = "no", base = "Caucasian")$Metric
   expect_equal(jfa_tprp[["metric"]][["all"]][["estimate"]], as.numeric(fairness_tpr[1, ]))
   expect_equal(jfa_tprp[["parity"]][["all"]][["estimate"]], as.numeric(fairness_tpr[2, ]))
-  # Negative predicted value parity
+  # Negative predictive value parity
   jfa_npvp <- model_fairness(compas, "Ethnicity", "TwoYrRecidivism", "Predicted", privileged = "Caucasian", positive = "yes", metric = "npvp")
   fairness_npv <- fairness::npv_parity(compas, outcome = "TwoYrRecidivism", preds = "Predicted", group = "Ethnicity", outcome_base = "no", base = "Caucasian")$Metric
   expect_equal(jfa_npvp[["metric"]][["all"]][["estimate"]], as.numeric(fairness_npv[1, ]))

vignettes/articles/algorithm-auditing.Rmd

@@ -68,7 +68,7 @@ metrics between an unprivileged protected class and a privileged protected class
 is referred to as parity, which quantifies relative fairness in the algorithm's
 predictions. Available parity metrics include predictive rate parity,
 proportional parity, accuracy parity, false negative rate parity, false positive
-rate parity, true positive rate parity, negative predicted value parity,
+rate parity, true positive rate parity, negative predictive value parity,
 specificity parity, and demographic parity [@friedler_2019; @pessach_2022]. The
 function returns an object that can be used with the associated `summary()` and
 `plot()` methods.

vignettes/articles/model-fairness.Rmd

@@ -28,7 +28,7 @@ algorithmic decision-making systems. It computes various model-agnostic metrics
 based on the observed and predicted labels in a dataset. The fairness metrics
 that can be calculated include demographic parity, proportional parity,
 predictive rate parity, accuracy parity, false negative rate parity, false
-positive rate parity, true positive rate parity, negative predicted value
+positive rate parity, true positive rate parity, negative predictive value
 parity, and specificity parity [@calders_2010; @chouldechova_2017;
 @feldman_2015; @zafar_2017; @friedler_2019]. Furthermore, the metrics are tested
 for equality between protected groups in the data.
@@ -346,12 +346,12 @@ model_fairness(
       privileged group (Caucasians). This indicates relatively fair treatment
       in the algorithm's true positive predictions.
 
-8. **Negative predicted value parity**: Compares the negative predicted value
+8. **Negative predictive value parity**: Compares the negative predictive value
   (e.g., for non-reoffenders) of each unprivileged group with that of the
   privileged group.
 
-    The formula for the negative predicted value is $NPV = \frac{TN}{TN + FN}$,
-      and the negative predicted value parity for unprivileged group $i$ is
+    The formula for the negative predictive value is $NPV = \frac{TN}{TN + FN}$,
+      and the negative predictive value parity for unprivileged group $i$ is
       given by $NPVP = \frac{NPV_{i}}{NPV_{privileged}}$.
 
     ```{r}
@@ -367,17 +367,17 @@ model_fairness(
     ```
 
     ***Interpretation:***
-    - *African American*: The negative predicted value parity for African
+    - *African American*: The negative predictive value parity for African
       Americans is 0.98013. A value close to 1 indicates that the negative
       predicted value for African Americans is very similar to the privileged
       group (Caucasians). This suggests fair treatment in predicting
       non-reoffenders among African Americans.
-    - *Asian*: The negative predicted value parity for Asians is 1.1163,
-      indicating that their negative predicted value is slightly higher than for
+    - *Asian*: The negative predictive value parity for Asians is 1.1163,
+      indicating that their negative predictive value is slightly higher than for
       Caucasians. This could suggest potential favoritism towards Asians in
       predicting non-reoffenders.
-    - *Hispanic*: The negative predicted value parity for Hispanics is 1.0326,
-      suggesting that their negative predicted value is slightly higher than for
+    - *Hispanic*: The negative predictive value parity for Hispanics is 1.0326,
+      suggesting that their negative predictive value is slightly higher than for
       Caucasians. This indicates potential favoritism towards Hispanics in
       predicting non-reoffenders.
 
@@ -472,12 +472,12 @@ The `fairness_selection()` function offers a method to select a fairness measure
 tailored to a specific context and dataset by answering the questions in the 
 developed decision-making workflow. The fairness measure that can be selected 
 include disparate impact, equalized odds, false positive rate parity, false 
-negative rate parity, predictive rate parity, equal opportunity, specificity 
-parity, negative predictive value parity, accuracy parity [@castelnovo_2022;
-@feldman_2015; @friedler_2019; @hardt_2016, @verma_2018]. After answering the 
-questions in the decision-making workflow and selecting the fairness measure to 
-apply, a graphical representation of the followed path can be created based 
-on the responses.
+negative rate parity, predictive rate parity, equal opportunity (also known as true positive 
+rate parity), specificity parity, negative predictive rate parity, accuracy parity 
+[@castelnovo_2022; @feldman_2015; @friedler_2019; @hardt_2016, @verma_2018]. 
+After answering the questions in the decision-making workflow and selecting the fairness 
+measure to apply, a graphical representation of the followed path can be 
+created based on the responses.
 
 As mentioned earlier, not all fairness measures are equally suitable for every 
 audit situation. For this reason, the `fairness_selection()` function we propose