and inexperience of examiners and chart interpreters, but the evidence that they have achieved this potential is meager. Porges and colleagues (1996) evaluated PolyScore and critiqued the methodology it used as unscientific and flawed. Notwithstanding the adversarial tone taken by Porges and colleagues, many of the flaws they identified apply equally to CPS, such as the lack of adequate evaluation.5

Dollins and associates (Dollins, Krapohl, and Dutton, 2000) compared the performance of these two algorithms with three other algorithms on an independent set of 97 selected confirmed criminal cases. CPS performed equally well on detection of both innocent and guilty subjects, while the other algorithms were better at detecting deceptive examinees than clearing nondeceptive ones. Unfortunately, the method of selecting these cases makes it difficult to interpret the reported rates of misclassification.

One could argue that computerized algorithms should be able to analyze the data better than human scorers because they incorporate potentially useful analytic steps that are difficult even for trained human scorers to perform (e.g., filtering and other transformations, calculation of signal derivatives), look at more information, and do not restrict comparisons to adjacent questions. Moreover, computer systems never get careless or tired. The success of both numerical and computerized systems, however, still depends heavily on the pretest phase of the examination. How well examiners formulate the questions inevitably affects the quality of information recorded.

PolyScore is currently working on algorithms for scoring the screening data coming from TES and relevant/irrelevant tests. An a priori base rate might be introduced in these algorithms to increase accuracy and to account for the low number of deceptive cases.

There has yet to be a proper independent evaluation of computer scoring algorithms on a suitably selected set of cases, for either specific incidents or security screening, which would allow one to accurately assess the validity and accuracy of these algorithms.



Some computerized systems store biographical information such as examinee’s name, social security number, age, sex, education, ethnicity, marital status, subject’s health, use of drugs, alcohol, and prior polygraph history (e.g., see, but it is unclear how this type of information would be appropriately used to improve the diagnostic accuracy of a computer scoring system.


Matte (1996) and Kircher and Raskin (2002) provide more details on the actual polygraph instruments and hardware issues and some of the history of the development of computerized algorithms.


Under the assumption of unequal variance for the two groups, which Kircher and

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