-
Law School Example
The law school example dataset, used to test the fairness of algorithms. -
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
The authors used two tabular datasets, Adult and Credit, that are ubiquitous in fairness literature, and transformed them into multi-label settings. -
Fair and Optimal Classification via Post-Processing
This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors. -
Fair and Private Post-Processing for Attribute-Aware Fair Regression
This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors. -
Differentially Private Post-Processing for Fair Regression
This paper describes a differentially private post-processing algorithm for learning attribute-aware fair regressors. -
ACSEmployment
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... -
Fair Generalized Linear Mixed Models
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... -
UCI datasets
The dataset used in the paper is a set of UCI datasets, including adult, census, covertype, financial, joke, mushroom, and statlog. -
Crime dataset
The dataset used in this paper is the Crime dataset, which is a real-world UCI Machine Learning Census dataset for violent state prediction. -
Fair Adversarial Instance Re-weighting (FAIR)
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... -
ACS-Income Dataset
The ACS-Income dataset is a dataset used for evaluating the fairness of machine learning models. It contains information about individuals and their income. -
Benchmark Fair Classification Dataset
The dataset used in the paper for fair subgroup mixup for improving group fairness. -
Law School Admission Bar Passage
The dataset used in the paper for fair subgroup mixup for improving group fairness. -
COMPAS Dataset
The COMPAS recidivism dataset contains 6,167 samples, and the task is to predict the recidivism of an individual based on criminal history, with the binary protected attribute... -
Bank Marketing dataset
The Bank Marketing dataset is a commonly used dataset in the fairness literature, containing information about individuals' demographic and economic characteristics. -
German dataset
The dataset used in this paper is the German dataset, which is a real-world UCI Machine Learning dataset extracted from a German bank for default prediction. -
Adult dataset
A commonly observed pattern in machine learning models is an underprediction of the target feature, with the model’s predicted target rate for members of a given category... -
Bank Marketing
The dataset comprises of phone call records of a marketing campaign run by a Portuguese bank. The marital status of the clients is considered feature to ensure fairness.
