  Machine learning based decision-support tools in criminal justice systems are
subjects of intense discussions and academic research. There are important open
questions about the utility and fairness of such tools. Academic researchers
often rely on a few small datasets that are not sufficient to empirically study
various real-world aspects of these questions. In this paper, we contribute
WCLD, a curated large dataset of 1.5 million criminal cases from circuit courts
in the U.S. state of Wisconsin. We used reliable public data from 1970 to 2020
to curate attributes like prior criminal counts and recidivism outcomes. The
dataset contains large number of samples from five racial groups, in addition
to information like sex and age (at judgment and first offense). Other
attributes in this dataset include neighborhood characteristics obtained from
census data, detailed types of offense, charge severity, case decisions,
sentence lengths, year of filing etc. We also provide pseudo-identifiers for
judge, county and zipcode. The dataset will not only enable researchers to more
rigorously study algorithmic fairness in the context of criminal justice, but
also relate algorithmic challenges with various systemic issues. We also
discuss in detail the process of constructing the dataset and provide a
datasheet. The WCLD dataset is available at
\url{https://clezdata.github.io/wcld/}.
