Moral Design of Algorithmic Governance
This manuscript argues that a government algorithmic enforcement system distributes the cost of official mistakes through its training loss function and deployment threshold. Together these settings form an error-allocation specification. The analysis treats that specification as a policy choice that may constitute a legislative rule under the Administrative Procedure Act and state analogues. It explains the persistence of inherited technical settings through choice-architecture defaults and institutional incentives. The proposed response is a penalty-default framework built around procurement-linked disclosure, legislative error budgets, public impact statements, periodic democratic review and a structured evidentiary presumption.
Abstract
When governments deploy algorithmic enforcement systems, they do not merely automate policy; they encode distributive choices about who bears the cost of institutional error. Every such system embeds an error-allocation specification: a loss function fixing the relative cost of false positives and false negatives in training and a decision threshold crystallizing those costs at deployment. This article classifies that specification as a legislative rule under the Administrative Procedure Act (APA) and its state analogues, such that adopting it through default procurement is unlawful subdelegation that bypasses notice and comment where the duty attaches. Behaviorally, these configurations persist as a choice-architecture problem: the default effect that drives organ-donation and retirement-savings behavior also operates, selectively, on the engineers who configure public-sector systems, entrenching configurations congenial to institutional incentives. It maps the second Mathews v. Eldridge factor onto error-cost allocation and pairs Mechanism Design with a democratic "Moral Design" framework and test. The argument yields five remedies ordered as a penalty-default architecture: error-allocation disclosure tied to procurement; legislative error budgets; Algorithmic Impact Statements with public comment; periodic democratic review; and a bifurcated Blackstone evidentiary presumption that reads the implied error-cost ratio off the disclosed configuration, administered by an Algorithmic Choice Architecture Review Board.
How the inquiry is constructed
The manuscript combines doctrinal analysis of administrative law, due process, equal protection and non-delegation with behavioural research on default effects. It also uses mechanism design and fairness scholarship to clarify the institutional choice. Documentary examples from benefit administration, criminal justice, border identity systems and public-sector procurement illustrate how error allocation enters deployed systems.
What the analysis establishes
- A training loss function and deployment threshold together encode a distributive choice about the costs of false positives and false negatives.
- That error-allocation specification can operate as a legislative rule rather than a merely technical configuration.
- Adoption through default procurement can bypass required public procedure and transfer effective policy choice to private or technical actors.
- Choice-architecture defaults help explain why inherited configurations persist when they align with institutional incentives.
- A penalty-default architecture can bring error allocation into a public record through disclosure, legislative adoption, impact statements, review and evidentiary rules.
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