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A Comprehensive Review and Future Prospects of Lie Detection Using Machine Learning

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Catalogue entry · Meta-Analyses & Systematic Reviews

A Comprehensive Review and Future Prospects of Lie Detection Using Machine Learning

Debanil Chanda, R. K. Mandal — Communications in Computer and Information Science,

2025Published
Key findings

Full text not accessible; unable to extract specific findings from the review.

Abstract

This 2025 review chapter by Chanda and Mandal examines the application of machine learning approaches to lie detection, published in the ICCTE 2023 conference proceedings. The full text was not accessible for detailed analysis.

Methodology

Review methodology could not be determined without access to the full chapter text.

Comprehensive study analysis

An in-depth, original analysis of this research study's methodology, findings, and significance for the polygraph profession.

Access Limitations

This chapter appears in a very recently published book (February 28, 2025) in the Communications in Computer and Information Science series (CCIS volume 2376). The chapter is available through Springer's paywall but no open-access preprint, author copy on ResearchGate, or institutional repository version could be located.

The DOI (https://doi.org/10.1007/978-3-031-81935-3_6) leads to Springer's platform, which requires institutional access or purchase. Without access to the actual paper's content—including its specific methodology, datasets reviewed, machine learning algorithms compared, accuracy metrics reported, and the authors' conclusions—I cannot provide the detailed, evidence-based analysis that this research database requires.

What Would Be Needed

To write a comprehensive analysis following the system instructions, I would need access to:

  • The specific deception detection modalities reviewed (polygraph, fMRI, EEG/P300, verbal analysis, etc.)
  • Machine learning algorithms and architectures compared
  • Dataset characteristics and sample sizes from reviewed studies
  • Reported accuracy rates and performance metrics
  • The authors' assessment of current limitations and future directions
  • Methodological details of the review process itself

Recommendation

If you have institutional access to Springer publications or can obtain the chapter through your library, please provide the full text or key excerpts, and I would be happy to complete a thorough analysis. Alternatively, if the authors have posted a preprint version on ResearchGate, arXiv, or a personal/institutional website, that link would enable me to proceed with the analysis.

Read the original study

The analysis above is original editorial content based on our review of this research. For the complete study including full data, methodology details, and author discussion, access the original publication below.

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