ACSIncome

doi:doi:10.57702/n9tjusor

ACSIncome

ACSIncome is one of the five pre-defined tasks in the Folktables dataset [17], which was recently collected to improve the older and commonly used UCI Adult Income dataset [19]. More specifically, we use the subset of Folktables dataset from the state of California, USA for 2018. The dataset contains a total of 195, 665 data points with 10 features each, where each data points represents an individual. The task is a binary classification to predict whether the individual’s income is above $50, 000. For fairness measures, we use perceived gender (Sex) as the sensitive attribute, which is also one of the 10 input features.

BibTex:

@dataset{Prakhar_Ganesh_and_Hongyan_Chang_and_Martin_Strobel_and_Reza_Shokri_2024,
    abstract = {ACSIncome is one of the five pre-defined tasks in the Folktables dataset [17], which was recently collected to improve the older and commonly used UCI Adult Income dataset [19]. More specifically, we use the subset of Folktables dataset from the state of California, USA for 2018. The dataset contains a total of 195, 665 data points with 10 features each, where each data points represents an individual. The task is a binary classification to predict whether the individual’s income is above $50, 000. For fairness measures, we use perceived gender (Sex) as the sensitive attribute, which is also one of the 10 input features.},
    author = {Prakhar Ganesh and Hongyan Chang and Martin Strobel and Reza Shokri},
    doi = {10.57702/n9tjusor},
    institution = {No Organization},
    keyword = {'ACSIncome', 'Binary Classification', 'Fairness in Machine Learning', 'Folktables'},
    month = {dec},
    publisher = {TIB},
    title = {ACSIncome},
    url = {https://service.tib.eu/ldmservice/dataset/acsincome},
    year = {2024}
}