Predicting tax avoidance by means of social network analytics
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AI systems might be employed in ways that remove taxpayers' ability to challenge decisions that may be wrong.
“or they may be used in ways that deprive taxpayers of their right to contest potentially wrongful decisions.11”
From Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard (2022) · cites this paper as [11]
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- Authors
- Lismont, J., Cardinaels, E., Bruynseels, L., De Groote, S., Baesens, B., Lemahieu, W., & Vanthienen, J.
- Affiliations
- KU Leuven (BE)
- Tilburg University (NL)
- University of Southampton (GB)
- Corresponding author
- Lismont, Jasmien
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 108
- Pages
- 13-24
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2793307483
- Cited by
- 51
- References
- 52
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 2 · 2023: 3 · 2022: 15 · 2021: 8 · 2020: 7 · 2019: 3 · 2018: 3
- FWCI
- 15.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Decision Support Systems (publishedVersion)
- Research portal (Tilburg University) (publishedVersion, other-oa)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- ePrints Soton (University of Southampton) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Vlaamse regering
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Corporate Taxation and Avoidance
- Social Sciences › Business, Management and Accounting › Accounting › Taxation and Legal Issues
- Social Sciences › Business, Management and Accounting › Accounting › Corporate Finance and Governance
- Keywords
- tax avoidance, social network analytics, corporate governance, network effects, random forest, director expertise, firm characteristics, EBITDA
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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