Artificial Intelligence for Deception Detection: A Multimodal Review of Methods, Challenges, And Ethical Perspectives

Within the realm of deception detection research, this comparative study investigates the use of machine learning, artificial intelligence, and multimodal data processing. From the year 2020 to the year 2024, it focuses on twenty-four studies that show the growing potential of AI-driven systems in terms of enhancing the consistency, scalability, and accuracy of fraud detection. In order to identify deceit in a variety of data types, such as facial expressions, audio signals, written language, and behavioral abnormalities, different techniques have showed promise. Some of these techniques include Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and hybrid models. On the other hand, issues like as adversary manipulation, biases in training datasets, and the potential for deception cues to be generalized across linguistic, cultural, and social contexts continue to be a concern. To further complicate the deployment of deception detection systems, there is a dearth of real-world validation, and the present models have little adaptability in dynamic environments. The article places an emphasis on the need of openness in the design of artificial intelligence, ethical concerns about user privacy, and the development of systems that have properties that are sensitive to cultural and environmental factors. The integration of concepts from other disciplines, the ability to withstand assaults from adversaries, and the development of ways to decrease prejudice should be the primary focus of research in the future.