The algorithmic muhtasib: an islamic moral-economy architecture for accountable AI governance in the digital state

Abstract

Governments worldwide are embedding AI into core public functions, yet dominant governance responses remain procedural — disclosure rules, impact assessments, audits — and are capacity-dependent, disadvantaging low- and middle-income digitizing states. This paper develops a framework pairing a normative foundation from the Islamic institution of hisba (public market and conduct oversight) with an architectural standard from resilience engineering, framed as a distributive and epistemic justice intervention for resource-constrained digitizing states. The paper proceeds conceptually.ing Three transferable principles are recovered from hisba — valuation transparency, distributed custodianship, documentary auditability — and a fourth criterion, contestability, is added from the algorithmic-accountability literature because the hisba sources speak to the inspection of a valuation mechanism rather than to the standing of an affected party to challenge a particular determination. Each of the four criteria is then paired with an architectural principle: disclosure with decision provenance, contestability with mandatory human override, distributed custodianship with bounded failure domains, documentary trace with continuous drift monitoring. The four paired criteria are given a minimal deontic representation as conditional obligations with threshold-triggered escalation. The framework is applied illustratively to algorithmic credit scoring and welfare targeting, and stress-tested across three stylized governance contexts of differing state capacity. The paired framework surfaces addressable governance gaps that neither a purely normative nor a purely architectural approach surfaces alone, and indicates that architectural accountability is comparatively capacity-robust: it reduces, without eliminating, the dependence of accountability on continuous discretionary oversight, shifting part of the compliance burden from ongoing institutional capacity to procurement-stage design requirements, while still requiring threshold-setting, system maintenance, human review, and enforcement. As a distributive and epistemic justice intervention for resource-constrained digitizing states, the framework offers a non-derivative theoretical contribution to comparative AI governance, pointing toward procurement-level architectural standards as a potentially more realistic lever than technical-assistance programs for improving accountability where oversight capacity is thinnest. The claim is analytical and remains to be tested empirically against deployed systems.