Confidence without Clarity

Deception detection is a critical yet challenging aspect of decision-making in law enforcement, legal contexts, and everyday interactions. Traditional methods, such as the Reid technique, are increasingly criticized for their reliance on behavioral cues and susceptibility to cognitive biases, often resulting in false confessions and wrongful convictions (Vrij et al., 2017; Taylor et al., 2021). Recent advancements in deception detection, particularly in AI-driven linguistic analysis and cross-cultural methodologies, have begun to address these limitations, offering more reliable and equitable approaches (Chatterjee et al., 2023; He et al., 2024; Kim et al., 2023). This study explores the relationship between self-assessed deception detection abilities and actual performance, hypothesizing that confidence is not a reliable predictor of accuracy and may exacerbate errors in judgment. Participants (N = 200), recruited through community outreach and professional networks across the United States, represented a diverse range of ages, educational backgrounds, and professional experiences. They self-assessed their deception detection skills before analyzing video interviews in which the truthfulness of suspects was later verified by substantial evidence. Using a mixed-methods approach, this study examines the interplay between confidence levels, reliance on verbal and non-verbal cues, and objective performance. The findings reveal a significant overconfidence bias, with participants frequently overestimating their abilities. This overconfidence was inversely related to accuracy, reflecting broader patterns of cognitive bias and decision-making errors in high-stakes environments (Maclean & Hancock, 2022). The results underscore the need for evidence-based training programs and policy reforms to mitigate bias and enhance ethical investigative practices. Practical recommendations include adopting validated approaches such as the PEACE model and integrating emerging tools like AI. For instance, recent studies demonstrate the utility of hybrid human-AI systems in improving decision-making accuracy while addressing algorithmic bias (Wu et al., 2020; Chatterjee et al., 2023). Moreover, AI-driven tools have shown promise in analyzing micro-expressions and speech patterns, providing more objective assessments of veracity while reducing systemic inequities caused by subjective misinterpretation of cultural differences (Kim et al., 2023; He et al., 2024). These findings emphasize the disproportionate impact of pseudoscientific methods on marginalized populations, who face heightened vulnerability to systemic injustices due to cultural and socioeconomic biases in traditional investigative practices. Future research will replicate this design exclusively with law enforcement professionals to investigate occupational influences on deception detection abilities. This approach aims to refine training programs by identifying how professional experience and structured methodologies shape accuracy and confidence. The findings are expected to inform policies on interrogation techniques, emphasizing strategies that improve detection accuracy while reducing ethical and procedural risks.