How do "technical" design-choices made when building algorithmic decision-making tools for criminal justice authorities create constitutional dangers?
It addresses the problem of ensuring algorithmic tools in criminal justice comply with public law and human rights, which is incremental as it builds on existing legal and data science insights.
The paper argues that technical design choices in machine-learning tools for criminal justice can lead to constitutional dangers, such as abuse of power and injustice, by showing how public law principles are often overlooked in development and implementation.
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.