This article provides a comprehensive analysis of cognitive biases in forensics and digital forensics, exploring how they impact decision-making processes in these fields. It examines various types of cognitive biases that may arise during forensic investigations and digital forensic analyses, such as confirmation bias, expectation bias, overconfidence in errors, contextual bias, and attributional biases.
The article also evaluates existing methods and techniques used to mitigate cognitive biases in these contexts, assessing the effectiveness of interventions aimed at reducing biases and improving decision-making outcomes. Furthermore, it introduces a new cognitive bias called “impostor bias” that may affect the use of generative Artificial Intelligence (AI) tools in forensics and digital forensics.
The impostor bias is the tendency to doubt the authenticity or validity of the output generated by AI tools, such as deepfakes, in the form of audio, images, and videos. This bias has the potential to lead to erroneous judgments or false accusations, undermining the reliability and credibility of forensic evidence.
The article discusses the potential causes and consequences of the impostor bias and suggests strategies to prevent or counteract it. By addressing these topics, the article offers valuable insights into understanding cognitive biases in forensic practices and provides recommendations for future research and practical applications to enhance objectivity and validity of forensic investigations.
Abstract:This paper provides a comprehensive analysis of cognitive biases in forensics and digital forensics, examining their implications for decision-making processes in these fields. It explores the various types of cognitive biases that may arise during forensic investigations and digital forensic analyses, such as confirmation bias, expectation bias, overconfidence in errors, contextual bias, and attributional biases. It also evaluates existing methods and techniques used to mitigate cognitive biases in these contexts, assessing the effectiveness of interventions aimed at reducing biases and improving decision-making outcomes. Additionally, this paper introduces a new cognitive bias, called “impostor bias”, that may affect the use of generative Artificial Intelligence (AI) tools in forensics and digital forensics. The impostor bias is the tendency to doubt the authenticity or validity of the output generated by AI tools, such as deepfakes, in the form of audio, images, and videos. This bias may lead to erroneous judgments or false accusations, undermining the reliability and credibility of forensic evidence. The paper discusses the potential causes and consequences of the impostor bias, and suggests some strategies to prevent or counteract it. By addressing these topics, this paper seeks to offer valuable insights into understanding cognitive biases in forensic practices and provide recommendations for future research and practical applications to enhance the objectivity and validity of forensic investigations.