  Scholars have focused on algorithms used during sentencing, bail, and parole,
but little work explores what we call carceral algorithms that are used during
incarceration. This paper is focused on the Pennsylvania Additive
Classification Tool (PACT) used to classify prisoners' custody levels while
they are incarcerated. Algorithms that are used during incarceration warrant
deeper attention by scholars because they have the power to enact the lived
reality of the prisoner. The algorithm in this case determines the likelihood a
person would endure additional disciplinary actions, can complete required
programming, and gain experiences that, among other things, are distilled into
variables feeding into the parole algorithm. Given such power, examining
algorithms used on people currently incarcerated offers a unique analytic view
to think about the dialectic relationship between data and algorithms. Our
examination of the PACT is two-fold and complementary. First, our qualitative
overview of the historical context surrounding PACT reveals that it is designed
to prioritize incapacitation and control over rehabilitation. While it closely
informs prisoner rehabilitation plans and parole considerations, it is rooted
in population management for prison securitization. Second, on analyzing data
for 146,793 incarcerated people in PA, along with associated metadata related
to the PACT, we find it is replete with racial bias as well as errors,
omissions, and inaccuracies. Our findings to date further caution against
data-driven criminal justice reforms that rely on pre-existing data
infrastructures and expansive, uncritical, data-collection routines.
