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.
翻译:学者们侧重于在判刑、保释和假释期间使用的算法,但很少工作探索我们所称的监禁期间使用的宫颈算法。本文侧重于宾夕法尼亚州Additive分类工具(PACT),用于对囚犯监禁期间的监禁水平进行分类。监禁期间使用的算法值得学者们更深入地关注,因为他们有权力颁布囚犯的活生生的现实。本案的算法决定了一个人可能承受更多的纪律行动,能够完成必要的程序拟定,并获得经验,这些经验除其他外被浓缩成融入假释算法的变数。鉴于这种能力,对目前被监禁的人使用的算法提供了独特的分析性观点,以思考数据和算法之间的辩证关系。我们对PACT的检查是双重和互补的。首先,我们对PACT的历史背景的定性概述表明,它旨在将恢复的能力和控制放在对康复的优先位置上。虽然它密切地告知囚犯的康复计划和假释考虑,但它植根于监狱安全的人口管理。第二,对146793年被监禁的人所使用的算法提供了一种独特的分析数据,而我们从错误的刑事分析,我们的数据与前的错误数据是,我们作为历史数据, 的错误的错误的,我们又从数据库中找到了。