#CPSE #Speaking_Events #HRFE #AI-ERADS ## Leveraging AI and Machine Learning to Transform Emergency Service In the evolving landscape of emergency services, the integration of Artificial Intelligence (AI) and Machine Learning (ML) presents unprecedented opportunities to enhance operational efficiency, resource allocation, and responder safety. This presentation will delve into Halifax Regional Fire & Emergency's pioneering research project funded by the Government of Canada through Defence Research and Development Canada (DRDC), in partnership with industry and academic institutions, aimed at revolutionizing emergency service decision support through advanced AI and ML technologies. Our technologies aim to: 1. Optimized Resource Allocation: Utilizing AI and ML to optimize emergency service dispatch, resource allocation, supply chain, and staffing. This is achieved by analyzing historical and real-time data from diverse sources, including systemic, open-source, and big data, to predict and allocate resources more effectively. 2. Enhanced Real-Time Decision Support: Implementing AI to monitor real-time radio communications using Natural Language Processing (NLP). This initiative seeks to provide relevant reference and systemic information to responders on-scene, thereby evaluating cognitive load reduction through improved human-system interfaces. 3. Address Cognitive Bias in May Day Incidents: Assessing the impact of AI-monitored NLP on reducing responder 'May Day' reaction times. By identifying and responding to critical audio commands in real-time, the project aims to enhance safety and efficiency during emergency operations by reducing cognitive bias. 4. Quality Analysis if Unstructured Data: A multitude of data collection including unstructured data such as caller and radio recording, dipatcher CAD notes, Incident Report narratives present untapped data sources to evaluate data trands, benchmarks and performance indicators with the abilty to use ML/AI to assess large quanties of disparate  and unstructured data. This research underscores the transformative potential of AI and ML in emergency services. By leveraging advanced technologies funded by a collaborative effort between government, industry, and academia, the project seeks to optimize resource allocation, enhance real-time decision support, quality analysis, and improve responder safety. Attendees will gain insights into the methodologies, challenges, and progress of integrating AI into emergency service operations. Keywords: Artificial Intelligence, Machine Learning, Emergency Services, Resource Allocation, Natural Language Processing, Real-Time Communication, Responder Safety, Incident Command, Fire Service.