在为刑事司法当局构建算法决策工具时，“技术”设计选择如何造成宪法危险？
How do "technical" design-choices made when building algorithmic decision-making tools for criminal justice authorities create constitutional dangers?
摘要：
这篇由两部分组成的论文认为，基于机器学习的算法工具的开发人员所做的看似“技术性”的选择可能会造成严重的宪法危险，增加滥用决策权的可能性以及不公正的范围和程度。利用正在使用或最近使用的三种算法工具来评估个人所构成的“风险”，以告知刑事司法当局应该如何对待他们，我们整合了数据科学和公法奖学金的见解，以展示公法原则和植根于这些原则的更具体的法律义务如何在算法工具的构建和实施中经常被忽视。我们认为，技术开发人员必须与公法专家密切合作，以确保如果算法决策支持工具要为刑事司法决策提供信息，则这些工具的配置和实施方式必须明显符合公法原则和学说，包括尊重人权。
英文摘要：
This two part paper argues that seemingly "technical" choices made by developers of machine-learning based algorithmic tools used to inform decisions by criminal justice authorities can create serious constitutional dangers, enhancing the likelihood of abuse of decision-making power and the scope and magnitude of injustice. Drawing on three algorithmic tools in use, or recently used, to assess the "risk" posed by individuals to inform how they should be treated by criminal justice authorities, we integrate insights from data science and public law scholarship to show how public law principles and more specific legal duties that are rooted in these principles, are routinely overlooked in algorithmic tool-building and implementation. We argue that technical developers must collaborate closely with public law experts to ensure that if algorithmic decision-support tools are to inform criminal justice decisions, those tools are configured and implemented in a manner that is demonstrably compliant with public law principles and doctrine, including respect for human rights, throughout the tool-building process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
