Package 'predfairness'

Adult Dataset

Description

Sample extracted from the 1994 USA census. Each observation is related to an individual.

Format

Data frame with 32,561 rows and 15 columns.

• income: factor, >50K / <=50K. Income of each person.

• age: integer, age of each person.

• workclass factor, category of work. Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.

• fnlwgt: numeric

• education: factor. Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.

• education_num: numeric. Variable education converted to numeric.

• marital-status: factor of marital status. Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.

• occupation: factor. Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.

• relationship:: factor. Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.

• race: factor. White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.

• capital_gain: numeric.

• capital_loss. numeric.

• hours-per-week: numeric. Worked hours per week.

• native-country:: factor. Country of origin.

Value

Returns a data frame with 32,561 rows and 15 columns.

Source

Dataset extracted from: UCL Machine Learning Repository

References

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Examples

data('adult.data')

---

Reject Option based Classification method

Description

Reject Option based Classification (ROC) method for discrimination reduction in predictive models. Given a probabilistic model for binary classifications, ROC is a post processing method which changes instances' classification labels in a defined probability interval. In a probabilistic model, the decision criteria is defined by simply choosing the category with the higher estimated probability. Considering a binary classification, a model returns two complementary probabilities.

Assuming that discriminatory classifications occurs near rejection boundary (when probabilities are near 0.5), the ROC method defines an interval in which probabilities can be considered next to the boundary. Then, once the interval size ([0.5, theta]) is defined, the method looks for the higher probability between the two classes. If a privileged person receives a positive classification with probability between 0.5 and theta, the method turn this classification to negative. Conversely, if the method finds a deprived person with negative classification probability between 0.5 and theta, then it changes her to positive.

Usage

roc_method(
  pred_mod,
  positive_col,
  positive_class,
  negative_col,
  sensible_col,
  privileged_group,
  classification_col,
  theta
)

Arguments

pred_mod	data frame - predictions and its probabilities with respect to each category.
positive_col	string - positive classification probabilities column name
positive_class	string - positive classification label
negative_col	string - negative classification probabilities column name
sensible_col	string - sensible attribute column name
privileged_group	string - privileged group label
classification_col	string - classifications column name
theta	numeric - classification probabilities threshold

Details

In a binary classification, the highest probability is always greater than 0.5. Considering already classified instances, and selecting people from the privileged-positive classified group and deprived-negative classified group, the method searches for those with the maximum probability less than theta. In this case, the function will change the instance's classification label and replace the two probabilities with their complementary. The user must run the data frame with predictions, the column name with the sensible attribute, as well as the privileged group name through the ROC method. Also, the user must add the classification column name, the categories probabilities columns names and the name of the category considered the positive one. This function returns a data frame with updated probabilities and classifications.

Value

Returns a new data frame with updated classifications and probabilities, maintaining the structure (columns and its names) of the original data frame, ran in the method.

Author(s)

Leonardo Paes Vieira

References

F. Kamiran, A. Karim and X. Zhang, "Decision Theory for Discrimination-Aware Classification," 2012 IEEE 12th International Conference on Data Mining, 2012, pp. 924-929, doi: 10.1109/ICDM.2012.45.

Examples

data('adult.data')

 adult.data$income = ifelse(test =  adult.data$income == '>50K',
                             yes = 1, no = 0)

 adult.data = adult.data[, colnames(adult.data) %in%
                           c('age', 'education', 'sex',
                             'income', 'capital_gain')]

 adult.data = adult.data[sample(1:nrow(adult.data), size = 100, replace = FALSE), ]

 ##### Logistic Regression

 if (!requireNamespace("stats", quietly = TRUE)) {
 stop("Package \"stats\" needed for this example to work.",
 call. = FALSE)}

 mod = glm(formula =  income ~., data = adult.data, family = binomial(link = 'logit'))

 ### The 'predict' function returns the classes probabilities
 ### automatically for caret (package) models

 pred = data.frame(greater = mod$fitted.values, less =  1 - mod$fitted.values, sex = adult.data$sex,
                   classification = ifelse(mod$fitted.values >= 0.5, 'greater', 'less'))

 theta = 0.6

 pred_changed = roc_method(pred_mod = pred, positive_col = 'greater',
                          positive_class = 'greater', negative_col = 'less',
                          sensible_col = 'sex', privileged_group = 'Male',
                          classification_col = 'classification',
                          theta = theta)


 pred_changed

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