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A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction

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  • Tala Talaei Khoei

    (Roux Institute Northeastern University)

  • Aditi Singh

    (Cleveland State University)

Abstract

Emotional Artificial Intelligence (Emotional AI), with its advanced capability to detect, analyze, and interpret human emotions, presents a groundbreaking opportunity for enhancing various aspects of policing and criminology. This paper delves into the integration of Emotional AI in these fields, highlighting its potential to revolutionize crime detection, prevention, and the improvement of interactions within the criminal justice system. By categorizing the applications of Emotional AI, from predictive policing to emotional assessments during interrogations, the paper explores how this technology can offer novel insights into criminal behavior and support mental health initiatives. Additionally, it addresses the ethical considerations associated with Emotional AI's deployment, such as privacy, bias, and the accuracy of emotion interpretation. The survey synthesizes current challenges and proposes future research directions, aiming to guide the responsible integration of Emotional AI technologies in law enforcement practices. The paper emphasizes the need for a balanced approach that respects individual rights while harnessing Emotional AI's benefits for justice and public safety.

Suggested Citation

  • Tala Talaei Khoei & Aditi Singh, 2024. "A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction," Journal of Computational Social Science, Springer, vol. 7(3), pages 2359-2402, December.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00313-3
    DOI: 10.1007/s42001-024-00313-3
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    References listed on IDEAS

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    1. Ho, Manh-Tung & Le, Ngoc-Thang B. & Mantello, Peter & Ho, Manh-Toan & Ghotbi, Nader, 2023. "Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude," Technology in Society, Elsevier, vol. 72(C).
    2. Tala Talaei Khoei & Naima Kaabouch, 2023. "Machine Learning: Models, Challenges, and Research Directions," Future Internet, MDPI, vol. 15(10), pages 1-29, October.
    3. Yu, Joanne & Dickinger, Astrid & So, Kevin Kam Fung & Egger, Roman, 2024. "Artificial intelligence-generated virtual influencer: Examining the effects of emotional display on user engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    4. Panagiotis Stalidis & Theodoros Semertzidis & Petros Daras, 2021. "Examining Deep Learning Architectures for Crime Classification and Prediction," Forecasting, MDPI, vol. 3(4), pages 1-22, October.
    5. Muzammil Khan & Azmat Ali & Yasser Alharbi & Gonzalo Farias, 2022. "Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques," Complexity, Hindawi, vol. 2022, pages 1-13, February.
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