Cyberdelegation and the Administrative State

Abstract

This paper explores questions and trade-offs arising from the delegation of administrative agency decisions to neural networks and similar examples of systems relying on technologies associated with the term "artificial intelligence", and offers the following preliminary observations to further discussion of the opportunities and risks. First, neither machine learning architectures such as deep learning neural networks nor expert systems presently are in a position to resolve – without human intervention – context-specific debates about society’s goals for regulation or administrative adjudication. These debates are often inherent in the implementation of statutes. Such debates about society's goals must also inform whether we assign value to aspects of human cognition that contrast with what computers can (presently) accomplish, or what might be conventionally defined as rational in a decision-theoretic sense. Second, society would do well to consider path-dependent consequences and associated cybersecurity risks that could arise from reliance on computers to make and support decisions. Such consequences include the erosion of individual and organizational knowledge over time. Third, it may prove difficult to limit the influence of computer programs even if they are meant to be mere decision support tools rather than the actual means of making a decision. Finally, heavy reliance on computer programs – particularly adaptive ones that modify themselves over time – may further complicate public deliberation about administrative decisions, because few if any observers will be entirely capable of understanding how a given decision was reached.

Keywords: delegation, regulation, administrative adjudication, artificial intelligence, algorithm, neural network, Mashaw, Weber, deliberation, Morgan v. United States