The Internal Revenue Service is expected to lean more
heavily on artificial intelligence technology as it carries out widespread
layoffs.
A report, released last week by the Treasury Inspector
General for Tax Administration, found the IRS could leverage its examination
results when using AI to select cases for further scrutiny and improve its
processes. TIGTA noted that the IRS’s current tax return selection models have
resulted in a high percentage of examinations that were completed with no
change to the tax liability. However, that means resources are wasted on
unproductive examinations and compliant taxpayers are unnecessarily burdened.
AI models can improve the process the IRS uses to select cases for
examination.
TIGTA assessed how effectively the IRS’s Large Business and
International Division and the IRS Small Business/Self-Employed Division employ
AI models to identify returns and issues for examinations.
The IRS started using AI several years ago and revamped how
it selects tax returns and identifies issues for examination by using AI models
that have been trained on current tax return data instead of relying on past
audit results.
The report noted that historical examination results are
informative and should be used by the IRS to monitor and improve AI models when
available. For instance, the IRS could utilize examination results to improve
return classification and return selection AI models that could potentially
identify new areas of noncompliance. The IRS should also consider evaluating
ensemble machine-learning for improving the accuracy of identifying
noncompliant taxpayers and narrowing the tax gap.
“Ensemble learning is an approach that combines
multiple machine-learning algorithms to potentially improve performance by
making more accurate predictions of which tax returns and/or issues to
examine,” said the report.