Popular entertainment portrays the lie-detection polygraph machine as a reliable source of information for catching criminal suspect. In reality, the polygraph machine and other lie-detection methods have yet to produce results that can overcome reasonable doubt. Although the research and testing of lie-detection methods has revealed fascinating insights, every approach falls short of delivering hard evidence into the hands of Illinois prosecutors.
Shortcomings of the polygraph
Everyone has watched a film or television show that connects sensors to a person and displays a read out of the subject’s physical reactions during questioning. This is the polygraph, and it is founded on the idea that telling a lie will be accompanied by physiological symptoms. Although this may be true, these signs of agitation are not exclusive to only telling lies.
Criteria and reality monitoring
Police interviewers may analyze answers given during interrogations based on criterion-based content analysis (CBCA) or reality monitoring (RM). With CBCA, researchers propose that people who tell the truth about real experiences use higher-quality details than descriptions of fake events. Similarly, RM theory puts forward that people recall more details from actual events than imaginary events. Real events enable people to give explanations with more nuances and sensory impressions.
Although CBCA and RM have shown some promise in research settings, challenges remain in how to evaluate responses. As a result, their accuracy rates fall short of the standards needed to prosecute people for felonies.
Artificial intelligence (AI)
As with most any part of human society these days, people have tried applying AI machine learning to lie detection. AI has proven to be an impressive detector of lies. It beats humans analyzing the same responses for truthfulness, but the technology still only has an accuracy rate below 70%.