Next Article in Journal
A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
Previous Article in Journal
U-SCAD: An Unsupervised Method of System Call-Driven Anomaly Detection for Containerized Edge Clouds
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Future Internet
Volume 17
Issue 5
10.3390/fi17050219
futureinternet-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Correction to Future Internet 2025, 17(1), 26.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Kalodanis et al. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26

by
Konstantinos Kalodanis
1,
Panagiotis Rizomiliotis
1,
Georgios Feretzakis
2,*,
Charalampos Papapavlou
3 and
Dimosthenis Anagnostopoulos
1
1
Department of Informatics & Telematics, Harokopio University of Athens, 17778 Athens, Greece
2
School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
3
Department of Electrical & Computer Engineering, University of Patras, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
Future Internet 2025, 17(5), 219; https://doi.org/10.3390/fi17050219
Submission received: 30 April 2025 / Accepted: 2 May 2025 / Published: 14 May 2025
Versions Notes
In the original publication [1], the authors identified that references [38,41] had become inaccessible. As a result, the authors replaced them with the following new references:
38.
O’Shea, J.; Crockett, K.; Khan, W.; Kindynis, P.; Antoniades, A.; Boultadakis, G. Intelligent Deception Detection through Machine Based Interviewing. In Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, 8–13 July 2018; IEEE: Piscataway, NJ, USA, 2018. ISSN 2161-4407. https://doi.org/10.1109/IJCNN.2018.8489392.
41.
Yildirim, S.; Chimeumanu, M.S.; Rana, A.Z. The influence of micro-expressions on deception detection. Multimed. Tools Appl. 2023, 82, 29115–29133.
Due to these changes, some content of Section 4.1. has been revised as stated below:
The sentence:
“Using a virtual border interview, it analyzed micro-expressions on a traveler’s face to assess the truthfulness of their answers, claiming an accuracy rate of approximately 76% during controlled trials [38].”
has been updated to:
“Using a virtual border interview, it analyzed micro-expressions on a traveler’s face to assess the truthfulness of their answers, claiming an accuracy rate of approximately 75% during controlled trials [38].”
Additionally, a citation for [38] has been added after the phrase “national watchlists”.
Another sentence:
“A failure analysis revealed that the AI-based lie detection system had a high false-positive rate, incorrectly flagging innocent travelers as deceptive in about 27% of cases [41].”
has been updated to:
“A failure analysis revealed that the AI-based lie detection system had a high false-positive rate, incorrectly flagging innocent travelers as deceptive in about 24.45% of cases [37].”
Finally, citation [41] has been added after the sentence ending with “behaviors may vary greatly”.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Kalodanis, K.; Rizomiliotis, P.; Feretzakis, G.; Papapavlou, C.; Anagnostopoulos, D. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kalodanis, K.; Rizomiliotis, P.; Feretzakis, G.; Papapavlou, C.; Anagnostopoulos, D. Correction: Kalodanis et al. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26. Future Internet 2025, 17, 219. https://doi.org/10.3390/fi17050219

AMA Style

Kalodanis K, Rizomiliotis P, Feretzakis G, Papapavlou C, Anagnostopoulos D. Correction: Kalodanis et al. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26. Future Internet. 2025; 17(5):219. https://doi.org/10.3390/fi17050219

Chicago/Turabian Style

Kalodanis, Konstantinos, Panagiotis Rizomiliotis, Georgios Feretzakis, Charalampos Papapavlou, and Dimosthenis Anagnostopoulos. 2025. "Correction: Kalodanis et al. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26" Future Internet 17, no. 5: 219. https://doi.org/10.3390/fi17050219

APA Style

Kalodanis, K., Rizomiliotis, P., Feretzakis, G., Papapavlou, C., & Anagnostopoulos, D. (2025). Correction: Kalodanis et al. High-Risk AI Systems—Lie Detection Application. Future Internet 2025, 17, 26. Future Internet, 17(5), 219. https://doi.org/10.3390/fi17050219

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop