New York Times: Computer Scientists Wield Artificial Intelligence to Battle Tax Evasion, by Lynnley Browning:
When federal authorities want to ferret out abusive tax shelters, they send an army of forensic accountants, auditors and lawyers to burrow into suspicious tax returns.
Analyzing mountains of filings and tracing money flows through far-flung subsidiaries is notoriously difficult; even if the IRS manages to unravel a major scheme, it typically does so only years after its emergence, by which point a fresh dodge has often already replaced it.
But what if that needle-in-a-haystack quest could be done routinely, and quickly, by a computer? Could the federal tax laws — 74,608 pages of legal gray areas and welters of credits, deductions and exemptions — be accurately rendered in an algorithm?
New academic research [Tax Non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood] seeks to use artificial intelligence to combat tax evasion by corporate entities, from publicly traded multinationals to private partnerships. The goal is to give the IRS a better way to investigate sophisticated tax shelters that strip tens of billions of dollars from federal coffers each year.
“We see the tax code as a calculator,” said Jacob Rosen, a researcher at the Massachusetts Institute of Technology who focuses on the abstract representation of financial transactions and artificial intelligence techniques. “There are lots of extraordinarily smart people who take individual parts of the tax code and recombine them in complex transactions to construct something not intended by the law.”
A recent paper by Mr. Rosen and four other computer scientists — two others from M.I.T. [Erik Hemberg & Una-May O’Reilly] and two at the Mitre Corporation [Geoff Warner & Sanith Wijesinghe], a nonprofit technology research and development organization — demonstrated how an algorithm could detect a certain type of known tax shelter used by partnerships [Tax Non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood]. ...
“It’s incredibly difficult to have a computer algorithm that duplicates the enormous creativity of taxpayers, but it’s very promising,” said Robert A. Green, a tax professor at Cornell Law School who read the M.I.T./Mitre paper.
For more, see Michael Hatfield (University of Washington), Taxation and Surveillance: An Agenda, 17 Yale J.L. & Tech. ___ (2015).
(Hat Tip: Francine Lipman.)