Algorithmic Tax Justice in Peru

Peru’s tax dispute system—administrative claim, Tax Court appeal, and contentious-administrative review—has increasingly migrated toward electronic files, e-invoicing, interoperable databases, and data-driven oversight. This article examines whether artificial intelligence can reduce avoidable tax litigation without weakening taxpayers’ rights and identifies the institutional conditions required to reconcile administrative efficiency with due process, reason-giving, and effective contestation. Using a legal-doctrinal and policy-analytical design, the study analyzes Peru’s tax dispute architecture, digital evidence environment, and AI-related risks in compliance and administrative litigation. The findings show that only bounded decision-support applications are institutionally appropriate, including audit triage, anomaly detection, document classification, workflow prioritization, compliance assistance, and consistency checks, provided that they do not replace legally attributable human judgment. AI is compatible with digital tax justice only when six safeguards are institutionalized: legally meaningful explainability, evidentiary and computational traceability, meaningful human oversight with override authority, lifecycle auditability, effective contestation, and distributional equality. The analysis further demonstrates that facially neutral digital requirements and risk models may generate unequal effects when disparities in connectivity, digital literacy, record-keeping capacity, and access to professional assistance translate into differences in audit exposure, compliance costs, evidentiary burdens, and practical contestability. The article proposes a rights-compatible framework for AI-supported tax enforcement in Peru.