Tuesday, May 16, 2023
Soled & Thomas: AI, Taxation, And Valuation
Jay A. Soled (Rutgers; Google Scholar) & Kathleen DeLaney Thomas (North Carolina; Google Scholar), AI, Taxation, and Valuation, 108 Iowa L. Rev. 651 (2023) (reviewed by Mirit Eyal-Cohen (Alabama; Google Scholar) here):
Virtually every tax system relies upon accurate asset valuations. In some cases, this is an easy identification exercise, and the exact fair market value of an asset is readily ascertainable. Often, however, the reverse is true, and ascertaining an asset’s fair market value yields, at best, a numerical range of possible outcomes. Taxpayers commonly capitalize upon this uncertainty in their reporting practices, such that tax compliance lags and the IRS has a difficult time fulfilling its oversight responsibilities. As a by-product of this dynamic, the Treasury suffers.
This Article explores how tax systems, utilizing artificial intelligence, can strategically address asset-valuation concerns, offering practical reforms that would help obviate this nettlesome and age-old problem. Indeed, if the IRS and Congress were to take advantage of this new and innovative technological approach, doing so would bode well for more accurate asset valuations and thereby foster greater tax compliance. Put somewhat differently, in the Information Era in which we exist, it is simply no longer true that accurate asset valuations are unattainable.