Procedural Contestability in Algorithmic Platform Enforcement: A Risk‑Tiered Framework for Commercial Automated Decision‑Making

Abstract

Artificial intelligence-driven automated decision-making (ADM) has become a constitutive feature of contemporary commercial markets. In platform governance, credit assessment, dynamic pricing, targeted advertising, and platform rule enforcement, automated systems increasingly determine who may participate in markets, on what terms, and with what degree of visibility. This development raises a distinctive legal challenge: the problem is not only that algorithms may produce inaccurate outcomes, but that they function as a form of private market governance that is opaque, scalable, and difficult to contest. Using algorithmic platform enforcement against business users as a central case, this paper argues that existing legal regimes, including data protection law, consumer and e-commerce law, contract law, competition law, and platform regulation, only partially address this problem. Each regime captures part of the issue, but none fully integrates algorithmic enforcement with procedural fairness and commercial market access. Methodologically, the paper combines doctrinal analysis, literature synthesis, and comparative legal analysis. Its central contribution is the development of procedural contestability as a regulatory principle for commercial ADM. Rather than relying on transparency as disclosure alone, it proposes a risk-tiered framework that links ex-ante preventive duties with ex-post procedural safeguards, including meaningful notice, reasoned explanation, accessible appeal, human review, and record preservation. The aim is to ensure that automated decisions with significant commercial consequences remain subject to legal standards of justification, review, and correction.