Systematic errors (biases) in applying verbal lie detection tools: richness in detail as a test case

Abstract

The current paper describes potential systematic errors (or biases) that may appear while applying content-based lie detection tools, by focusing on richness in detail – a core indicator in verbal tools – as a test case. Two categories of biases are discussed: those related to the interviewees (i.e., interviewees with different characteristics differ in the number of details they provide when lying or telling the truth) and those related to the tool expert (i.e., tool experts with different characteristics differ in the way they perceive and interpret verbal cues). We suggested several ways to reduce the influence of these biases, and emphasized the need for future studies in this matter.

Key words:

• Richness in detail
• judgmental biases
• verbal lie detection tools
• reality monitoring
• criteria based content analysis
• scientific content analysis

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the Israel Science Foundation [grant number 372/14].