THE USE OF BIG DATA ANALYTICS BY THE IRS:
EFFICIENT SOLUTION OR THE END OF PRIVACY AS WE KNOW IT
While many express concerns about private industry’s analytics programs which amass browsing and spending information, few seem to be aware of the government’s involvement in big data and predictive analytics. As the government has a lot more control over your rights and obligations, it would seem that this activity should be reviewed and monitored.
In a paper published last year, we examine the privacy issues resulting from the IRS’s big data analytics program as well as the potential violations of federal law. Although historically, the IRS chose tax returns to audit based on internal mathematical mistakes or mismatches with third party reports (such as W-2s), the IRS is now engaging in data mining of public and commercial data pools (including social media) and creating highly detailed profiles of taxpayers upon which to run data analytics. We argue that current IRS practices, mostly unknown to the general public are violating fair information practices. This lack of transparency and accountability not only violates federal law regarding the government’s data collection activities and use of predictive algorithms, but may also result in discrimination. While the potential efficiencies that big data analytics provides may appear to be a panacea for the IRS’s budget woes, unchecked, these activities are a significant threat to privacy. Other concerns regarding the IRS’s entrée into big data are raised including the potential for political targeting, data breaches, and the misuse of such information. This article is intended to bring attention to these privacy concerns and contribute to the academic and policy discussions about the risks presented by the IRS’s data collection, mining and analytics activities.
Houser, Kimberly A. and Sanders, Debra, The Use of Big Data Analytics by the IRS: Efficient Solution or the End of Privacy as We Know it? (March 29, 2017). Vanderbilt Journal of Entertainment & Technology Law, Vol. 19, No. 4, 2017. Available at SSRN: https://ssrn.com/abstract=2943002