Paul L. Caron

Friday, January 6, 2023

Using AI To Find Tax Minimization Strategies

Andrew Blair-Stanek (Maryland; Google Scholar), Nils Holzenberger (Institut Polytechnique de Paris) & Benjamin Van Durme (Johns Hopkins; Google Scholar), Shelter Check: Proactively Finding Tax Minimization Strategies via AI, 177 Tax Notes Fed. 1515 (Dec. 12, 2022):

Tax Notes Federal (2022)In this article, the authors explore how artificial intelligence could be used to automatically find tax minimization strategies in the tax law. Congress or Treasury could then proactively shut down such strategies. But, if large accounting or law firms develop the technology first, the result could be a huge, silent hit to the treasury.

We have proposed a new approach to using AI in tax law. Rather than the consensus bottomup approach of feeding the torrent of data the IRS receives into AI models, we propose the topdown approach of understanding the text of all tax law authorities and modeling how these authorities may be manipulated in new and unusual ways to minimize taxes. Our approach has several advantages, including being proactive, allowing outside researchers to help, and using the actual text of tax law authorities without requiring human lawyers to manually encode them. But our approach has a dark side —it also might be used by tax advisers to find new strategies. To counter that, we propose that the IRS immediately make tax strategies found using AI into reportable transactions.

We should note that the novel top-down approach we propose is not mutually exclusive with the bottom-up approach. Combining the two may turn out to be the most powerful approach to attacking tax minimization. For example, topdown semantic parses of tax law authorities and models of possible strategies may be entered into bottom-up models that review reams of tax return data. Conversely, the plentiful data available to the IRS might be used as input to help train topdown models.

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