The September 2026 issue of Tax Research carries an article on how artificial intelligence makes tax administration smarter. The authors argue that AI lowers the cost of interaction between tax authorities and taxpayers, reconnects fragmented data flows and shifts supervision from watching invoices to watching data. They also warn that uneven data quality, opaque algorithms, tight computing capacity and poor cross-agency sharing remain real obstacles. Their proposals are concrete: standardise tax-related data and build cross-agency sharing mechanisms; require explainable algorithms and keep humans in the loop in enforcement; consolidate computing resources with a focus on self-reliance; and update legal rules and contingency mechanisms so the technology genuinely serves modern governance.
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AI-Empowered Tax Administration Under Data-Driven Governance: Logic, Challenges and Paths
An article in the September 2026 issue of Tax Research examines how artificial intelligence is reshaping tax administration in China. It argues that AI improves collection efficiency by cutting compliance costs on both sides, rebuilding data workflows and shifting governance models, but still faces constraints in data quality, algorithmic transparency, computing capacity and cross-agency sharing. The authors propose standardised tax data governance, a trustworthy algorithm framework with human-machine collaboration, consolidated and self-controlled computing infrastructure, and updated legal and resilience rules.
2026-11-02