adult {mlr3fairness}

Adult Dataset

Description

Dataset used to predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset Train dataset contains 13 features and 30178 observations. Test dataset contains 13 features and 15315 observations. Target column is "target": A binary factor where 1: <=50K and 2: >50K for annual income. The column "sex" is set as protected attribute.

Derived tasks

• adult_train: Original train split for the adult task available at UCI.

• adult_test: Original test split for the adult task available at UCI.

Using Adult - Known Problems

The adult dataset has several known limitations such as its age, limited documentation, and outdated feature encodings (Ding et al., 2021). Furthermore, the selected threshold (income <=50K) has strong implications on the outcome of analysis, such that "In many cases, the $50k threshold understates and misrepresents the broader picture" (Ding et al., 2021). As a result, conclusions w.r.t. real-world implications are severely limited.

We decide to replicate the dataset here, as it is a widely used benchmark dataset and it can still serve this purpose.

Pre-processing

• fnlwgt Remove final weight, which is the number of people the census believes the entry represents

• native-country Remove Native Country, which is the country of origin for an individual

• Rows containing NA in workclass and occupation have been removed.

• Pre-processing inspired by article: @url https://cseweb.ucsd.edu//classes/sp15/cse190-c/reports/sp15/048.pdf

Metadata

• (integer) age: The age of the individuals

• (factor) workclass: A general term to represent the employment status of an individual

• (factor) education: The highest level of education achieved by an individual.

• (integer) education_num: the highest level of education achieved in numerical form.

• (factor) marital_status: marital status of an individual.

• (factor) occupation: the general type of occupation of an individual

• (factor) relationship: whether the individual is in a relationship-

• (factor) race: Descriptions of an individual’s race

• (factor) sex: the biological sex of the individual

• (integer) captain-gain: capital gains for an individual

• (integer) captain-loss: capital loss for an individual

• (integer) hours-per-week: the hours an individual has reported to work per week

• (factor) target: whether or not an individual makes more than $50,000 annually

Source

Dua, Dheeru, Graff, Casey (2017). “UCI Machine Learning Repository.” http://archive.ics.uci.edu/ml/. Ding, Frances, Hardt, Moritz, Miller, John, Schmidt, Ludwig (2021). “Retiring adult: New datasets for fair machine learning.” In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).

Examples

library("mlr3")
data("adult_test", package = "mlr3fairness")
data("adult_train", package = "mlr3fairness")