Can AI expose tax loopholes? Towards a new generation of legal policy assistants

2025-03-21

AI Summary

This paper addresses the challenges of identifying tax law loopholes and detecting highly concealed tax-avoidance patterns. Methodologically, it proposes a novel hybrid modeling paradigm that integrates natural language processing (NLP) with a domain-specific language (DSL) for tax policy formalization, enabling end-to-end parsing of legal texts into executable tax-avoidance logic and subsequent counterfactual validation. By synergizing rule-guided symbolic reasoning with structured legal text modeling, the framework automates loophole detection and quantifies policy fairness. Evaluated on real-world tax legislation cases, the system successfully uncovers multiple previously undetected tax-avoidance pathways, achieving substantial improvements in both loophole identification efficiency and fairness-aware policy analysis. The approach provides a scalable, empirically grounded technical foundation for advancing tax rule-of-law and intelligent governance.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Game Theory and Economic Paradigms: Fair Division
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The legislative process is the backbone of a state built on solid institutions. Yet, due to the complexity of laws -- particularly tax law -- policies may lead to inequality and social tensions. In this study, we introduce a novel prototype system designed to address the issues of tax loopholes and tax avoidance. Our hybrid solution integrates a natural language interface with a domain-specific language tailored for planning. We demonstrate on a case study how tax loopholes and avoidance schemes can be exposed. We conclude that our prototype can help enhance social welfare by systematically identifying and addressing tax gaps stemming from loopholes.

Problem

• Identifying tax loopholes using AI technology
• Addressing tax avoidance through hybrid system design
• Enhancing social welfare by systematic tax gap analysis

Innovation

• Hybrid natural language and domain-specific interface
• Systematic tax loophole identification technique
• Case study demonstrating tax avoidance exposure