Lie Detection and Blindness: Research in Pandemic Times

The aim of this article is to explore the differences in lie detection between sighted and visually impaired people. In the study, three groups of blind and sighted individuals were tested on their lie-detecting abilities during natural everyday communication. Due to the current pandemic situation, the study was conducted in accordance with the sanitary regime, using appropriate methods and tools. The results revealed no statistically significant differences between blind and sighted individuals in the accuracy of lie and truth detection. The groups did not differ in how confident they were in making veracity judgements either. The study shows that visual impairment does not have an impact on lie-detection abilities and that blind people are as good at detecting lies as sighted individuals.

A NOVEL MACHINE LEARNING-BASED APPROACH FOR IDENTIFICATION OF UNREALISTIC TAX RETURNS BY EEG SIGNAL PROCESSING

For many years, the uncertainty of lie-detection systems has been one of the concerns of tax organizations. Clearly, the results of these systems must be generalized by a high value of accuracy to be acceptable by related systems to identify tax fraud. In this paper, a new method based on P300-based component has been proposed for detection of tax fraud. To this end, the test protocol is designed based on Odd-ball paradigm concealed information recognition. This test was done on 40 people and their brain signals were acquired. After prepossessing, the classic features are extracted from each single trial. After that, time–frequency (TF) transformation is applied on the sweeps and TF features are produced thereupon. Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, guilty and innocent persons are classified by K-nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. We found that combination of time–frequency and classic features has better ability to achieve higher amount of accuracy to identify the unrealistic tax returns. The obtained results show that the proposed method can detect deception by the accuracy of 91% which is better than other previously reported methods. This study, for the first time, succeeded in presenting a novel method for identifying unrealistic tax returns through EEG signal processing, which has significantly improved the yield of this study compared to the previous literature.

Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset

Lie detection is considered a concern for everyone in their day-to-day life, given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and to their visual appearance, including the face, to find any signs that indicate whether or not the person is telling the truth. While automatic lie detection may help us to understand these lying characteristics, current systems are still fairly limited, partly due to lack of adequate datasets to evaluate their performance in realistic scenarios. In this work, we collect an annotated dataset of facial images, comprising both 2D and 3D information of several participants during a card game that encourages players to lie. Using our collected dataset, we evaluate several types of machine learning-based lie detectors in terms of their generalization, in person-specific and cross-application experiments. We first extract both handcrafted and deep learning-based features as relevant visual inputs, then pass them into multiple types of classifier to predict respective lie/non-lie labels. Subsequently, we use several metrics to judge the models’ accuracy based on the models predictions and ground truth. In our experiment, we show that models based on deep learning achieve the highest accuracy, reaching up to 57% for the generalization task and 63% when applied to detect the lie to a single participant. We further highlight the limitation of the deep learning-based lie detector when dealing with cross-application lie detection tasks. Finally, this analysis along the proposed datasets would potentially be useful not only from the perspective of computational systems perspective (e.g., improving current automatic lie prediction accuracy), but also for other relevant application fields, such as health practitioners in general medical counselings, education in academic settings or finance in the banking sector, where close inspections and understandings of the actual intentions of individuals can be very important.

Detecting Lies is a Child (Robot)’s Play: Gaze-Based Lie Detection in HRI

Robots destined to tasks like teaching or caregiving have to build a long-lasting social rapport with their human partners. This requires, from the robot side, to be capable of assessing whether the partner is trustworthy. To this aim a robot should be able to assess whether someone is lying or not, while preserving the pleasantness of the social interaction. We present an approach to promptly detect lies based on the pupil dilation, as intrinsic marker of the lie-associated cognitive load that can be applied in an ecological human–robot interaction, autonomously led by a robot. We demonstrated the validity of the approach with an experiment, in which the iCub humanoid robot engages the human partner by playing the role of a magician in a card game and detects in real-time the partner deceptive behavior. On top of that, we show how the robot can leverage on the gained knowledge about the deceptive behavior of each human partner, to better detect subsequent lies of that individual. Also, we explore whether machine learning models could improve lie detection performances for both known individuals (within-participants) over multiple interaction with the same partner, and with novel partners (between-participant). The proposed setup, interaction and models enable iCub to understand when its partners are lying, which is a fundamental skill for evaluating their trustworthiness and hence improving social human–robot interaction.

The world of lie detection: a study into state of lie detection usage by state and society in Asia, Africa and Europe

Thought privacy, that is, the right to control who has access to one’s thoughts, is a bedrock of human civilization as we know it today. Lie detection technologies, with their severe limitations, have proliferated in use all over the world. Such unsupervised proliferation of technology that claims to infer human psychological states has huge implications for the future of human existence and rights. We collate data about usage of lie detection technologies for six categories of use in all sovereign countries of Asia, Africa and Europe. The collected data, being category specific, also provides insight into what kind of uses are more prevalent. The present paper provides an idea about the enormity and spread of the problem of lie detection technology proliferation. We also provide observations about legal control mechanisms of such issues. This data provides a baseline for further observation and monitoring of the field of lie detection technology proliferation.

Mistakes, Overconfidence, and the Effect of Sharing on Detecting Lies

Mistakes and overconfidence in detecting lies could help lies spread. Participants in our experiments observe videos in which senders either tell the truth or lie, and are incentivized to distinguish between them. We find that participants fail to detect lies, but are overconfident about their ability to do so. We use these findings to study the determinants of sharing and its effect on lie detection, finding that even when incentivized to share truthful videos, participants are more likely to share lies. Moreover, the receivers are more likely to believe shared videos. Combined, the tendency to believe lies increases with sharing. (JEL C91, D83, D91, L82)

Lady Justice may be Blind, but is She Racist? Examining Brains, Biases, and Behaviors Using Neuro-Voir Dire

This paper discusses the possible use of functional magnetic-resonance imaging as potentially useful in jury selection. The author suggests that neuro-voir could provide greater impartiality of trials than the standard voir, while also preserving existing privacy protections for jurors. He predicts that ability to image and understand a wide range of brain activities, most notably bias-apprehension and lie detection, will render neuro-voir dire invaluable. However currently, such neuro-solutions remain preliminary.

Sadder and Less Gullible?—Investigating Depressive Realism Effects in Judgments of Veracity

Introduction: Depressive realism literature suggests that depressed individuals’ negative self-view is correlated with less self-serving positivity bias. Also, research suggests some social cognitive advantages for individuals with subclinical levels of depression (dysphoria), especially in identifying negative emotions. This study tested the hypothesis that individuals with dysphoric symptoms show less of a truth bias and are more accurate at detecting deception. Moreover, this effect was expected to be stronger in positive statements (I like) than in negative (I dislike) statements. Finally, a lower judgment confidence and a more accurate assessment of their lie detection ability were expected to be found in individuals with dysphoric symptoms. Methods: Two hundred-sixty-nine participants judged the veracity of 24 video statements. Analyses tested the hypotheses with three different measures of depression: the IPIP-240 Depression Subscale, the PHQ-9, and the DESC-I. Results: In contrast to the assumptions, results found no evidence that individuals with dysphoric symptoms were better at identifying false and true messages in general. While higher scores of the DESC-I were negatively correlated with accuracy in lie detection, the IPIP-240 and the PHQ-9 were found to be not significantly correlated with lie detection accuracy. While for like statements individuals with dysphoric symptoms and individuals without (measured with the DESC-I) were not different in accuracy, individuals with dysphoric symptoms had lower accuracy scores in dislike statements than individuals without. Moreover, the PHQ-9 found lower measures of judgment confidence in individuals with dysphoric symptoms compared to individuals without, while the other depression measurements showed no significant differences. Furthermore, no evidence for a more accurate assessment of lie detection ability in individuals with dysphoric symptoms was found. Discussion: Results and directions for future research are discussed.

A Critical Review of Cognitive Load Lie detection

This article critically examines the idea that cognitive load interventions can expose lies. I discuss the theoretical weaknesses of seven popular justifications of the cognitive load approach; for example, that liars must suppress the truth while lying, and this handicap makes lying challenging. Each of those seven justifications exhibits significant limitations. Moreover, the theoretical fitness of each justification is variable and unclear. A thematic review further indicated that researchers substantially rely on the customary seven justifications to support the cognitive load approach despite the shortcomings. This article proposes several research questions whose answers could help ascertain the theoretical fitness of the seven justifications and the cognitive load approach.