The COMPAS dataset includes the processed COMPAS data between 2013-2014.
The data cleaning process followed the guidance in the original COMPAS repo.
Contains 6172 observations and 14 features.
The target column could either be "is_recid" or "two_year_recid", but often "two_year_recid" is prefered.
The column "sex" is set as protected attribute, but more often "race" is used.
Derived tasks:
compas: A classification task for the compas data set with the protected attribute 'sex'.
compas_race_binary: A classification task for the compas data set with the protected attribute 'race'. The observations have been filtered, keeping only observations with race"Caucasian"and"African-American". The protected attribute has been set to"race".
Source
ProPublica Analysis: https://github.com/propublica/compas-analysis
Bao, Michelle, Zhou, Angela, Zottola, A S, Brubach, Brian, Desmarais, Sarah, Horowitz, Seth A, Lum, Kristian, Venkatasubramanian, Suresh (2021). “It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks.” In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).
Using COMPAS - Known Problems
The COMPAS dataset was collected as part of the ProPublica analysis of machine bias in criminal sentencing. It is important to note, that using COMPAS is generally discouraged for the following reasons:
The prediction task derived from this dataset has little connection to actually relevant tasks in the context of risk assessment instruments.
Collected data and labels suffer from disparate measurement bias.
The dataset should therefore not be used to benchmark new fairness algorithms or measures. For a more in-depth treatment, see Bao et al., 2021: It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. We replicate the dataset here to raise awareness for this issue. Furthermore, similar issues exist across a wide variety of datasets widely used in the context of fairness auditing and we, therefore, consider issues, e.g. derived from disparate measurement bias an important issue in the context of fairness audits.
Pre-processing
Identifying columns are removed
Removed the outliers for abs(days_b_screening_arrest) >= 30.
Removed observations where is_recid != -1.
Removed observations where c_charge_degree != "O".
Removed observations where score_text != 'N/A'.
Factorize the features that are categorical.
Add length of stay (c_jail_out - c_jail_in) in the dataset.
Pre-processing Resource:@url https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb
Metadata
(integer) age : The age of defendants.
(factor) c_charge_degree : The charge degree of defendants. F: Felony M: Misdemeanor
(factor) race: The race of defendants.
(factor) age_cat: The age category of defendants.
(factor) score_text: The score category of defendants.
(factor) sex: The sex of defendants.
(integer) priors_count: The prior criminal records of defendants.
(integer) days_b_screening_arrest: The count of days between screening date and (original) arrest date. If they are too far apart, that may indicate an error. If the value is negative, that indicate the screening date happened before the arrest date.
(integer) decile_score: Indicate the risk of recidivism (Min=1, Max=10)
(integer) is_recid: Binary variable indicate whether defendant is rearrested at any time.
(factor) two_year_recid: Binary variable indicate whether defendant is rearrested at within two years.
(numeric) length_of_stay: The count of days stay in jail.