How to Argue with an Algorithm: Lessons from the COMPAS ProPublica Debate
How to Argue with an Algorithm: Lessons from the COMPAS ProPublica Debate
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如何与算法争论：COMPAS ProPublica 辩论的经验教训
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发表时间：
2019
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通讯作者：
A. Washington
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作者：
A. Washington
美国通过算法优化其不断增长的刑事司法系统的效率，然而，法律的学者忽视了如何构建关于算法预测的法庭辩论。在州诉卢米斯案中，辩方辩称，法院在量刑时考虑风险评估违反了正当程序，因为算法预测的准确性无法得到验证。威斯康星州最高法院支持在量刑时考虑预测风险，因为评估是公开的，被告可以通过验证输入算法的数据的准确性来质疑预测。
法院关于如何与算法争论的观点正确吗？
卢米斯法院忽略了在算法中处理数据的计算程序。算法如何计算数据与计算数据的质量同样重要。卢米斯的论点揭示了需要新的推理形式来证明基于证据的工具的逻辑。“数据科学推理”可以提供一些方法来质疑预测算法的完整性，并以该技术的工作原理为依据。
这篇文章的贡献是一系列的论点，可以支持有关预测算法的正当程序索赔，特别是替代制裁的惩教罪犯管理分析（“COMPAS”）风险评估。作为一个全面的治疗，本文概述了正当程序的论点在卢米斯，分析了正在进行的学术辩论中的参数COMPAS，并提出了替代的算法的组织背景下的参数。
风险评估已经成为新兴数据科学领域内最早的广泛学术辩论之一。ProPublica的调查记者声称COMPAS算法存在偏见，并将他们的发现作为开放数据集发布。ProPublica的数据引发了一场关于风险评估的多产和专业性的对话，以及关于算法社会影响的更广泛的对话。ProPublica-COMPAS的辩论反复考虑了三个主题：公平的数学定义，模型的可解释性解释以及人口比较组的重要性。
虽然卢米斯案的判决涉及在量刑时允许使用风险评估，但对组织内部日常实践的更深入理解可能会将有关算法的辩论扩展到有关采购、实施或培训的问题。购买风险评估的刑事司法组织最有资格证明一个人的评估如何与为其行政需要设计的算法相匹配。接受风险评估的人无法猜测算法如何对他们进行排名，因为他们不知道为什么他们被归类在某个群体中，以及什么标准控制着排名。关于风险评估算法的争议暗示了程序性正当程序是否是以行政能力运作的刑事司法系统自动化的成本。
The United States optimizes the efficiency of its growing criminal justice system with algorithms however, legal scholars have overlooked how to frame courtroom debates about algorithmic predictions. In State v Loomis, the defense argued that the court’s consideration of risk assessments during sentencing was a violation of due process because the accuracy of the algorithmic prediction could not be verified. The Wisconsin Supreme Court upheld the consideration of predictive risk at sentencing because the assessment was disclosed and the defendant could challenge the prediction by verifying the accuracy of data fed into the algorithm.
Was the court correct about how to argue with an algorithm?
The Loomis court ignored the computational procedures that processed the data within the algorithm. How algorithms calculate data is equally as important as the quality of the data calculated. The arguments in Loomis revealed a need for new forms of reasoning to justify the logic of evidence-based tools. A “data science reasoning” could provide ways to dispute the integrity of predictive algorithms with arguments grounded in how the technology works.
This article’s contribution is a series of arguments that could support due process claims concerning predictive algorithms, specifically the Correctional Offender Management Profiling for Alternative Sanctions (“COMPAS”) risk assessment. As a comprehensive treatment, this article outlines the due process arguments in Loomis, analyzes arguments in an ongoing academic debate about COMPAS, and proposes alternative arguments based on the algorithm’s organizational context.
Risk assessment has dominated one of the first wide-ranging academic debates within the emerging field of data science. ProPublica investigative journalists claimed that the COMPAS algorithm is biased and released their findings as open data sets. The ProPublica data started a prolific and mathematically-specific conversation about risk assessment as well as a broader conversation on the social impact of algorithms. The ProPublica-COMPAS debate repeatedly considered three main themes: mathematical definitions of fairness, explainable interpretation of models, and the importance of population comparison groups.
While the Loomis decision addressed permissible use for a risk assessment at sentencing, a deeper understanding of daily practice within the organization could extend debates about algorithms to questions about procurement, implementation, or training. The criminal justice organization that purchased the risk assessment is in the best position to justify how one individual’s assessment matches the algorithm designed for its administrative needs. People subject to a risk assessment cannot conjecture how the algorithm ranked them without knowing why they were classified within a certain group and what criteria control the rankings. The controversy over risk assessment algorithms hints at whether procedural due process is the cost of automating a criminal justice system that is operating at administrative capacity.