28 datasets found

The UCI Adult Census dataset was extracted from the 1994 Census bureau database, gathering 32,561 instances represented by 9 features such as age, education and occupation. The...

A decision can be defined as fair if equal individuals are treated equally and unequals are treated unequally. Adopting this definition, the task of designing machine learning...

The dataset used in the paper is not explicitly described, but it is mentioned that it contains sensitive attributes such as gender or race.

The dataset contains medical information of various patients collected for research purposes. The race attribute is considered for fairness.

The COMPAS dataset’s prediction task is to calculate the recidivism outcome, indicating whether individuals will be rearrested within two years after the first arrest, with race...

The Fair-ONB method is a novel undersampling proposal based on data morphology.

The law school example dataset, used to test the fairness of algorithms.

The authors used two tabular datasets, Adult and Credit, that are ubiquitous in fairness literature, and transformed them into multi-label settings.

This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors.

This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors.

This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors.

ACSEmployment is another one of the five pre-defined tasks in the Folktables dataset [17]. We use the same subset from the state of California, USA for 2018 as above. The...

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]....

The training data often in obtained from social surveys. In social surveys, oftentimes the data collection process is a strata sampling, e.g. due to cost restrictions. In strata...

The dataset used in the paper is a set of UCI datasets, including adult, census, covertype, financial, joke, mushroom, and statlog.

The dataset used in this paper is the Crime dataset, which is a real-world UCI Machine Learning Census dataset for violent state prediction.

The proposed framework was tested on four datasets, three of which are commonly used benchmarks. Two datasets (German credit and Adult income) come from the UCI ML repository...

The ACS-Income dataset is a dataset used for evaluating the fairness of machine learning models. It contains information about individuals and their income.

The dataset used in the paper for fair subgroup mixup for improving group fairness.

The dataset used in the paper for fair subgroup mixup for improving group fairness.