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Policy Forum: Algorithmic Arm's-Length--AI-Based Risk Scoring and Procedural Fairness in Transfer-Pricing Audits

Author

Listed:
  • Theertha Narayanan

    (Georgetown Law, Washington, DC)

  • Pramod Kumar Siva

    (Texas A&M University School of Law, College Station, Texas)

Abstract

This article examines how the use of artificial intelligence (AI)-based risk scoring for transfer-pricing audits by the Canada Revenue Agency (CRA) interacts with Canadian administrative law and emerging international AI governance. The article argues that the transfer-pricing reforms introduced in the 2025 federal budget--stricter methodologies, expanded recharacterization powers, higher penalty thresholds, and shortened documentation deadlines--heighten the duty of procedural fairness in audit selection. The article reviews doctrinal standards under Vavilov and Dow Chemical, compares CRA practice with OECD guidance and foreign developments, and assesses whether algorithmic selection can remain justified, intelligible, and transparent. It concludes by proposing a governance code for AI-based tax audits to reconcile efficiency with legality and taxpayer rights.

Suggested Citation

  • Theertha Narayanan & Pramod Kumar Siva, 2026. "Policy Forum: Algorithmic Arm's-Length--AI-Based Risk Scoring and Procedural Fairness in Transfer-Pricing Audits," Canadian Tax Journal, Canadian Tax Foundation, vol. 74(2), pages 419-433.
    • Handle: RePEc:ctf:journl:v:74:y:2026:i:2:p:419-433
    DOI: https://doi.org/10.32721/ctj.2026.74.2.pf.narayanan
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