Public administration has adopted artificial intelligence to increase its efficiency in the 21st century. The developing world is presenting new challenges, which need strategies for disaster prevention and speedy response. Public administrators are especially putting the effort in prevention than dealing with the effects. Police use cameras to monitor speed before accidents occur, the meteorology department uses drones to collect data on likely hurricanes, floods, or sandstorms, among others. The government is investing in prevention since it is less expensive. The utilization of artificial intelligence also solves the problem of facilities being understaffed. The idea should, however, not be confused about the loss of jobs by the employees. Artificial intelligence (AI) does what can be done without human presence, leaving employees to do the work that requires human expertise (Frank et al., 2019). AI reduces the workload and increases efficiency; it does not replace humans. The adoption of advanced technology provides data for better prediction on the likelihood of disaster or crime, which makes prevention easier in Australia.
The Rationale of the Study
In 2017 alone, the total costs were 306 billion dollars for hurricanes, earthquakes, and wildfires worldwide (Rathi, 2017). Between 1994 and 2013, 6,873 natural hazards occurred globally, killing 1.35 million people or nearly 68,000 lives yearly. With investment in artificial intelligence, the numbers are expected to reduce significantly. The government has adopted AI, but a significant percentage of employees do not understand how to maximize the use of the equipment.
The developing world is presenting new challenges, which need strategies for disaster prevention and speedy response. The utilization of artificial intelligence solves the problem of facilities being understaffed. The idea should, however, not be confused about the loss of jobs by the employees.Given the challenges of implementing artificial intelligence interventions in public administration, the assessment will provide an insight into the extent of hand-on skills that employers have and how it affects the utilization of AI. The research will also offer figures that indicate whether the government's investment inexpensive technology is more beneficial than utilizing the traditional methods of disaster prevention. Comparing the cost of machines used for AI versus the cost of recovering from the loss, not considering the loss of human lives, will indicate the effectiveness of AI in public administration.
Public safety officials and development planers monitor data on infrastructures such as bridges and roads using sensors and devices. The data from this equipment determines whether the construction of new infrastructure is necessary or not. They also gather data on when repairs are necessary, instead of waiting for disaster to strike and catch the government by surprise and cause loss of lives. The assessment also includes whether the infrastructure can stand a likely natural hazard. For instance, equipment to measure the speed of the wind and drones are used to assess the likelihood of a hurricane. The government then advises citizens within the zones that will be affected on how to stay safe, secure their homes, and what time to avoid being outside. The data generated from this equipment also tells the path that a hurricane will take, so the government prepares possible evacuation routes, and knows what areas to attend to first, in terms of priority (Van, 2014). An example was Duke Energy, when 20,000 professionals were staged across the Carolinas to respond to Hurricane Florence, which saved thousands of lives. The same data is also useful in giving updates on what is failing and where the responders need backup.
Similarly, the Australian Federal Police can adopt the same idea in crime scenes. In case an officer is injured, the sensors can sense motion or lack of it, heartbeat, breathing rate, etc. which are indicators of the activities at the scene. When the device does not record movement, records an increased heartbeat, and strained to breathe, it could be a signal that the officer needs back up. For instance, in Dubai, the police use an integrated security system that reduces deaths due to traffic accidents, monitors residential and industrial for crime, through artificial intelligence (Halaweh, 2018). The system initiates an immediate response from the police before anyone calls 911.
The first 72 hours of a disaster are the most intense, and determine how many lives are saved. Satellite images have proven especially valuable in tracing people after an emergency. The data reduces wasted effort and prioritizes response in line with the severity. According to Imran et al., (2014), one such application is the use of Artificial Intelligence Disaster Response (AIDR), software that collects filters and classifies tweets that are related to an ongoing mission during a crisis. Similar to the system is the Facebook "Mark Yourself Safe" system. Areas that are most affected by the disaster will also be prioritized during planning, such that the effects are felt less significantly. Natural disasters cannot be prevented, but experiencing them while prepared is less severe.
Challenges of Implementing Artificial Intelligence
The utilization of artificial intelligence solves the problem of facilities being understaffed. The idea should, however, not be confused about the loss of jobs by the employees. Artificial intelligence (AI) does what can be done without human presence, leaving employees to do the work that requires human expertise. AI reduces the workload and increases efficiency; it does not replace humans. Artificial intelligence (AI) does what can be done without human presence, leaving employees to do the work that requires human expertise. AI reduces the workload and increases efficiency; it does not replace humans. Despite the excitement that comes with the idea of AI, implementing requires humans to understand its impacts and challenges of implementation, especially in public administration. The government is highly bureaucratic and often takes time to assimilate new practices. The stakeholders involved must, therefore, be made to understand why the new methods are more important than traditional methods. Public offices also have strict auditors, so the process must be cost-efficient. All interventions must also be easy to integrate into the system for seamless integration since the operations have to keep running (Adixon, 2019).
The workplace is also faced with the challenge of having four generations in the same environment: Traditionalist, Baby boomers, Generation X, and Millennial (Adixon, 2019). While the generation X and Millennial generation have technology as the go-to- option, the former two prefer to stick to their old ways. Teams are also more diverse, with both real office teams and virtual teams. The number of people who work from home is also increasing tremendously. Such are the considerations that should be considered when implementing AI. Technology is also fast-changing, so the workplace must be ready to keep adjusting to new technology changes. Besides changing fast, AI is also expensive, so the facility must have sufficient funds to start and maintain the project.
Research Design and Techniques
The research will be both mixed and exploratory, as a parameter to ensure the accuracy of results. Mixed research utilizes both qualitative and quantitative methods of data collection. Qualitative methods are non-numerical and seek to explain why. They seek the opinion and feelings of people, thus are likely to be biased, and need another research method for them to be effective. Quantitative methods involve numbers, calculations, and analysis. Qualitative methods include looking at records and past data on the topic of concern and noting their trends over the years. A research design that produces the least margin of error is chosen. Analyzing available qualitative data gives a conclusive decision on the opinions expressed during interviews.
The researcher will find data to analyze the success of AI in public administration. Data will be collected from the Australian Disaster Response unit. The data will include records of loss of lives in the past, before and after the use of AI. The topic will be well researched since technology is a vast field, and what works in one set up may not work in another. The researcher will also explore the shortcomings of artificial intelligence in disaster management. This information will be necessary for other scientists who wish to apply the gathered knowledge in research on AI elsewhere
One of the most helpful features of conducting mixed methods research is the use of triangulation. Combining the two methods of research ensures the strengths of another compensate for the weaknesses of one. Using the two sources of data ensures the findings of the research are void of bias. Semi-structured interviews with responders and their seniors will also be utilized. The method is suitable for gaining subjective information. Interviewing responders and the management will gather precise information on whether the funding, implementation, and results of artificial intelligent are impressive. They will be chosen for the study since they have firsthand information. The interview with each participant should take about an hour and will be voluntary.
Purpose of the Study
The purpose of this study is to assess the importance of using artificial intelligence in disaster management for mitigation, preparedness, response, and recovery from disaster. The review of records from the disaster response department will provide data to analyze by comparing how the use of AI has helped mitigate or reduce the effect of disasters.
The research will be concluded by providing recommendations to the government on what needs improvement and how employees and citizens feel about the use of artificial intelligence in public administration. The results will also provide seniors with feedback from employees on how they perceive their duties, and what can be improved in the workplace.
Data Collection Procedures
Literature review on existing technology in the field of disaster management from the department. Interviews with emergency responders and their seniors in the department
Strengths and Weaknesses of the Adopted Research Methods
Using the mixed method of data is an accurate method of research that minimizes errors due to bias. However, it is never void of challenges and shortcomings. However, the strengths outdo the weaknesses. This method allows for triangulation. Triangulation allows the researcher to view the topic from different vantage points. What is not accounted for by one method is taken care of by another. For instance, in this research, the researcher will acquire qualitative data, use questionnaires, then interview officers. The officers are experienced in this field, so they will offer the best of information on the challenges they face. Involving the staff that operates in the field and AI department will also ensure the information is not biased. In cases where there have been disparities in information, this method also helps clear it. Conducting interviews also assures the researcher of honesty to some extent. When conducting an interview, the time the respondent takes their facial expression and body movements can predict how honest an answer is.
Despite its strengths, however, the mixed method of research has a few challenges. The method can be complex to carry out, especially when choosing the two methods to combine. In this study, it will not be...
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