AI in Adjudication and Administration

The use of artificial intelligence has expanded rapidly in recent years across many aspects of the economy. For federal, state, and local governments in the United States, interest in artificial intelligence has manifested in the use of a series of digital tools, including the occasional deployment of machine learning, to aid in the performance of a variety of governmental functions. In this paper, we canvas the current uses of such digital tools and machine-learning technologies by the judiciary and administrative agencies in the United States. Although we have yet to see fully automated decision-making find its way into either adjudication or administration, governmental entities at all levels are taking steps that could lead to the implementation of automated, machine-learning decision tools in the relatively near future. Within the federal and state court systems, for example, machine-learning tools have yet to be deployed, but other efforts have put in place digital building blocks toward such use. These efforts include the increased digitization of court records that algorithms will need to draw upon for data, the growth of online dispute resolution inside and outside of the courts, and the incorporation of non-learning risk assessment tools as inputs into bail, sentencing, and parole decisions. Administrative agencies have proven much more willing than courts to use machine-learning algorithms, deploying such algorithmic tools to help in the delivery of public services, management of government programs, and targeting of enforcement resources. We discuss already emerging concerns about the deployment of artificial intelligence and related digital tools to support judicial and administrative decision-making. If artificial intelligence is managed responsibly to address such concerns, the use of algorithmic tools by governmental entities throughout the United States would appear to show much future promise. This status report on current uses of algorithmic tools can serve as a benchmark against which to gauge future growth in the use of artificial intelligence in the public sector