Attitude of Emergency Dispatchers Towards Artificial Intelligence – A Black Box of Expectations

Abstract

Introduction: AI is transforming various industries, especially healthcare and emergency services. For example, AI helps with clinical decision support, detects cardiac arrest and stroke during calls, and manages text-to-speech translation. On the human-centered side, the societal and personal impacts of AI and other technologies are significant but under-researched. Therefore, this study examines the belief systems of emergency dispatchers regarding AI applications. Methods: From September 2021 to September 2023, eight extensive interviews were conducted with a total of 31 individuals, lasting over 619 minutes. Following grounded theory, the interview guide was iteratively adapted to support theory development. Results: The interviews revealed a high level of commitment to their profession and a strong appreciation and interest in research. While many issues within public safety and answering points (PSAPs) and the healthcare system were identified, few concrete ideas for AI-based solutions were mentioned. In addition to the common assumption of high mental workload in emergency call centers and the need for AI systems to be understandable, there are notable differences in the belief systems of dispatchers and other experts. These differences often lead to a more negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications. However, the ability to reflect can mitigate these limitations. AI can support dispatchers who have to handle complex tasks under time pressure, information deficits and uncertainty. Conclusion: In addition to the assumption of high mental workload and the need for understandable AI systems, dispatchers and other experts have different belief systems. These can lead to a negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications, although reflection can help to mitigate this. AI can support dispatchers to handle complex tasks under pressure, information deficits and uncertainty. To prevent rejection of AI and raise awareness of its opportunities and risks, a comprehensive package of measures such as the one we have introduced is needed.