skip to main content
10.1145/3396956.3397002acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesdg-oConference Proceedingsconference-collections
poster

Open Government Data for Machine Learning Tax Recommendation

Published: 16 June 2020 Publication History

Abstract

Taxpayers may be interested in overpayment and which group of taxpayers he or she belongs to. Government officials may be concerned with underpaying taxpayers for auditing purposes and group taxpayers in the rapidly changing society. Machine learning and data mining techniques have been applied to provide solutions to these taxation related queries. Classification algorithms allow predicting the tax bracket based on the taxpayers' attributes. The regression model allows to predict the tax estimate so that the overpayment or underpayment can be determined. Clustering algorithms group taxpayers so that they can be compared to the past year tax brackets. Finally, feature selection allows finding salient attributes to predict the tax and tax bracket. In this article, New York State's Open Tax Data is used to demonstrate the machine learning and data mining algorithms and identify issues of using them. Furthermore, various visualization techniques are to present the discovered information to both taxpayers and government officials.

References

[1]
Jung An and Ned Wilson. 2016. Tax Knowledge Adventure: Ontologies that Analyze Corporate Tax Transactions. In Proceedings of the 17 th International Digital Government Research Conference (dg.o 2016) on Digital Government Research. ACM 2016, 303-311.
[2]
Jung An, James Geller, Yi-Ta Wu, and Soon Ae Chun. 2007. Semantic Deep Web: Automatic Attribute Extraction from the Deep Web Data Sources. In Proceedings of the 22nd Annual ACM Symposium on Applied Computing (SAC 2007). ACM Press, 1667-1672.
[3]
2019. Future of Tax - How Artificial Intelligence Will Likely Change Tax Compliance. https//medium.com/emtax/future-of-tax-how-artificial-intelligence-will-likely-change-tax-compliance-da1fbc268f7b, accessed April 2020.
[4]
York State Department of Taxation and Finance. 2019. Income Tax Components by Size of Income by Place of Residence: Beginning Tax Year 1999.

Cited By

View all
  • (undefined)Linked Open Government Data to Predict and Explain House Prices: The Case of Scottish Statistics PortalSSRN Electronic Journal10.2139/ssrn.4123599

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
dg.o '20: Proceedings of the 21st Annual International Conference on Digital Government Research
June 2020
389 pages
ISBN:9781450387910
DOI:10.1145/3396956
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2020

Check for updates

Author Tags

  1. Data Visualization
  2. Income Tax
  3. Machine Learning
  4. Open Data
  5. Open Government

Qualifiers

  • Poster
  • Research
  • Refereed limited

Conference

dg.o '20

Acceptance Rates

Overall Acceptance Rate 150 of 271 submissions, 55%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)1
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (undefined)Linked Open Government Data to Predict and Explain House Prices: The Case of Scottish Statistics PortalSSRN Electronic Journal10.2139/ssrn.4123599

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media