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Artificial Intelligence and Machine Learning in Crisis Communication: A Management Information System Perspective

Author

Listed:
  • Muhammed Zakir Hossain

    (Associate Professor, Department of Business Studies, State University of Bangladesh, Bangladesh)

  • Nasrin Akter

    (Stanton University, United States of America)

  • Latul Hasan

    (International American University, United States of America)

  • Muhim Bepari

    (Pacific States University, United States of America)

  • Samia Sultana

    (Southern California State University, United States of America)

Abstract

This paper investigates the function of Artificial Intelligence (AI) and Machine Learning (ML) in augmenting crisis communication, analyzing their applications, efficacy, and obstacles to their implementation. Utilizing a mixed-methods approach that encompasses case studies of significant crises such as the COVID-19 pandemic, Hurricane Katrina, and the Australian bushfires, alongside expert interviews and a survey of professionals, the research underscores how AI/ML tools enhance decision-making speed, public sentiment analysis, and real-time communication during crises. Nonetheless, the study also reveals considerable impediments, including ethical dilemmas, data privacy concerns, and the necessity for human oversight in AI-driven decision-making. The research concludes that although AI and ML possess substantial potential to transform crisis communication, addressing these challenges necessitates meticulous integration, ethical frameworks, and improved infrastructure.

Suggested Citation

  • Muhammed Zakir Hossain & Nasrin Akter & Latul Hasan & Muhim Bepari & Samia Sultana, 2025. "Artificial Intelligence and Machine Learning in Crisis Communication: A Management Information System Perspective," Post-Print hal-05736603, HAL.
    • Handle: RePEc:hal:journl:hal-05736603
    DOI: 10.59324/ejiss.2025.1(3).12
    as

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