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Application of AI and Machine Learning In Safety Performance Management Systems and Measure.
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Application of AI and Machine Learning In Safety Performance Management Systems and Measures
Overview
Artificial intelligence (AI) and machine learning (ML) are revolutionizing the world in terms of technology. The two technologies are being tested in different types of fields such as speech recognition, custom machine learning models, data-centric environment, Chabot interaction, and image and text analysis (Alpaydin, 2016). All these elements could enable the AI journey for security and public safety in an organization. Today organizations all over the globe are leveraging workplace design, technology, and culture to deliver a better experience to workers. Machine learning and artificial intelligence are ensuring organizations have bespoke and personalized experiences that drive productivity and influence behavior through the career of a person. AI and machine learning are also being used in the field of healthcare, to solve complex problems and make predictions using vast amounts of data. The two technologies are influencing every part of our lives, for instance, well-reputed companies such as Amazon, Apple, Google, Microsoft, IBM, and Facebook have invested a lot in ML and AI (Brougham & Haar, 2018). Some of the recent trends in AI are Facial recognition despite its immense popularity; it has sparked many debates worldwide. AI-enabled chips, the cloud, and deep learning are other significant trends in AI. The algorithms and the data used to train and model AI systems are becoming more powerful every day. People argue that AI and machine learning are just but tools, while in the real sense, they are becoming more knowledgeable and surpass human abilities. When you look from all perspectives, these two are technological components and should be engineered with safety. On the other hand, AI and machine learning can be integrated into safety management systems to prevent or reduce incidents and hazards. Safety Performance Management Systems should be integrated into our everyday processes throughout companies and organizations. The purpose of this descriptive, qualitative research study is to analyze the application of AI and Machine Learning in Safety Performance Management Systems and Measures. More technological advancements in AI and machine learning will revolutionize the world and provide safer technology.
Background Information
There are many debates around the globe on ‘what AI is’ and ‘what AI is not.’ Studies show that there is more hype when it comes to reality than there is to reality. Many governments around the world are pouring huge amounts of money in artificial intelligence and machine learning. Governments are publishing high-end reports and predictions about AI and ML and their contribution towards Productivity and GDP. The one question people are asking themselves, ‘is it worth taking AI and ML seriously? According to research, AI should be taken seriously despite the many disputes around the world. According to Brougham & Haar (2018), Artificial intelligence should be taken as a burden and, on the other hand, a good thing. This is because artificial intelligence is making a machine behave and thing an intelligent human being. There are many definitions of AI, but the best correct one would be the techniques associated with pattern recognition and data analysis. AI and ML can be differentiated from other digital technologies in that machine learning, and Artificial intelligence learns from the environment to make a correct and accurate decision.
On the other hand, machine learning is a sub-area of AI, whereby IT systems can find solutions by checking patterns in a database. Machine learning enables information technology systems to recognize patterns on data sets and existing algorithms and develop an adequate solution to problems. In Machine learning, knowledge is mainly generated from the basis of experience. For instance, for software to generate solutions, data and algorithms must be feed into the system on time. The data analysis rules must also be defined to ensure the data output is accurate (Langley & Simon, 1995). Machine learning works similarly to human learning; for instance, when a child is learning, we use images of different objects. From this, they can differentiate and identify the type of objects. Machine learning works in the same principle as a child. Machine learning is essential because it helps people work efficiently and creatively.
According to Aziz & Dowling (2019), safety professionals often consider the impact of regulatory and internal policy changes on the well-being of employees. Research shows that OSHA spends billions of dollars when enforcing safety policies and advocating groups to push for changes in how employees and employers interact. Still, research shows that majority of the safety improvements are due to technological advancements such as AI and ML around the world. Safer robotics, tools, and information technology have led to safer and better working conditions for the workforce. Computer enhanced machines such as robot arms and drones have taken dangerous tasks to complete work faster and reduce risk and incidents (Russell, Markov, & Neller, 2006). Artificial intelligence has officially entered our world completely; for instance, just look around our lives, the smart speaker, virtual assistant on the phone, smart television. We can all agree that artificial intelligence is everywhere in our lives. Artificial intelligence can also be seen in workplaces; for instance, organizations are now using AI-powered Chabot is that instantly reply and chat with customers 24/7.
According to Genesereth & Nilsson (2012), more than 1.5 million individuals around the world suffer from work-related injuries and illnesses. Furthermore, employee injuries resulted in organization compensation and lost working days. Computer technology has come at the right time, ensuring there is a reduction in work-related stress and fewer physical injuries. Human factors play a significant role when it comes to workplace safety, with stress and fatigue being the main contributors to accidents. The findings from the research show that more than 40% of American workers suffer from fatigue while working. This has contributed to a majority of incidents and injuries. One major benefit of having AI in the Safety Performance Management System is its inability to get unwell, tired, or stressed. In short, through Artificial intelligence, human factors can be scaled down in organizations. For instance, in 2018, an intelligent automated system for Assuring safe working environment was launched. This artificial intelligence system could detect if an employee is wearing the correct and required PPE when working. The system blends innovative algorithms, video footage, and machine learning to check if a person is suitably dressed in a work area (Reddy, 1988). If the AI system detects nothing, then it sends, a restrict and alert access message to the worker.
The increasing ubiquity of Artificial intelligence and computer systems has a dramatic effect on all industries and society. It is ubiquitous to see a computer system entrusted with functions, which human beings can depend on. For instance, healthcare facilities are now using embedded computers for heart pacemakers, fly-by-wire flight control systems for airliners, and anti-lock braking systems. Artificial intelligence is being integrated with Safety Management Systems to ensure industries such as nuclear industries are safe (Russell & Norvig, 2002). Studies show that computers allow systems to operate in ways that are impossible if humans operated them. Through performance management systems, systems have a shorter reaction time compared to humans. For instance, during an incident, a human might pause for a moment to think, but for a safety performance management system within a split of a second, orders are executed. Artificial intelligence embedded in computer systems allows 24/7 online monitoring ensuring safety for everybody in an organization. Safety Performance Management Systems are now able to monitor everything in a control room, from temperature to humidity.
Through artificial intelligence, safety will be on another level in the next couple of years. As of now, not highly-priced cars are now embedded with artificial intelligence, and they can sense when a collision is about to take place (Jordan & Mitchell, 2015). This shows the level of technology is growing, and every industry will have a safety management system that is embedded with Artificial intelligence. On the other hand, machine learning is being used in nuclear-powered rooms to control room temperature. Machine learning collects all the data of what is taking place, and with the combination of artificial intelligence, it makes decisions. There was a time. Artificial intelligence was science fiction, which could only be seen in movies. Findings show that the availability of data has helped in the breakthrough of AI.
Research design
The following research used qualitative study and systematic review to gather relevant data on the Application of AI and Machine Learning in Safety Performance Management Systems and Measures. A qualitative study will ensure we gain an understanding of the underlying motivations, opinions, and reasons for Artificial intelligence and machine learning. A qualitative study will help to develop the ideas and provide insights for the underlying problem in artificial intelligence. Through Qualitative study, we will uncover trends in opinion and thought and will dive dipper into how safety management systems have improved because of machine learning and artificial intelligence (Lee, Tsung & Wu, 2018). Qualitative research was chosen as the best study for the investigation because it involves a process known as induction, where information of a specific area is collected. The researcher studies different data that constructs from different theories and concepts.
Participants
Participants used in the research were random people from a booth beside the road. The participants were used for observation purposes. The participants were required to ask questions to smartphones embedded with artificial intelligence. The smartphones had assistants, namely google, Alexa, and Bipsy. The participants were to prove that these smartphones could answer their random questions. The participants were also shown a security system in a house that only recognized the owner through facial recognition. The participants were also able to tell a smart speaker to play music and do many numerous things. The participants were also shown how construction workers check higher places using drones. The participant’s feedback was very good for the researcher. They all believed that artificial intelligence was the next big technological advancement in Safety performance management systems.
Data collection methods
The following are some of the methods used for data collection techniques. The methods were used because they collect relevant and adequate data. The data collected will address the research question of the study. Nonetheless, the main method used in the research is a qualitative research method
Library research
This was by far the best method used when collecting data for this study. According to Harrell & Bradley (2009), Library research is a method that deals with the analysis of evidence such as documents and historical records. Library research allows the gathering of information from the library material, which includes; conference proceedings, textbooks, both unpublished and published academic documents such as these journals and dissertations. Internet research is also another method that can be used as library research. The data gathered from the library sources can be categorized as secondary data. By secondary data, it means everybody besides the researcher can use the data. Secondary data is not original because it is obtained from unpublished and published sources. Library research is the best data collection method for this research because the majority of Artificial intelligence information is written down in books, articles, journals, and website blogs. Research people show that people learn of artificial intelligence through library research because the majority of people are not exposed to Artificial intelligence devices.
Observation
This was another method used to collect data for the research. Observation one of the best methods for collecting data because it is a passive data collection method. Observation allows the researcher to observe and collect data through photos, notes, and video/audio recordings (Harrell & Bradley, 2009). Observation would be the best for this research because, in order to believe there is Artificial intelligence, then one has to observe. Through observation, participants were given smart Amazon and Google smart speakers. The smart speakers are embedded with Artificial intelligence and can talk when given commands (Rowlinson, 2004). The participants were also provided with smartphones that have google assistants, Alexa and Bibpsy, to investigate how Artificial works. The participants were also taken to a smart home to see different artificial intelligence systems such as the security system that recognizes the owner of the house through facial recognition.
Interviews
While collecting data through observation, face-to-face interviews were also used. While the participants observed AI smartphones, speakers, and smart security systems. They were in the process asked simple questions as part of the interview. The participants were asked random questions to ensure they were familiar with will Artificial intelligence and machine learning. The researcher jotted down the participant’s feedback. The participants were asked if artificial intelligence would solve safety issues in organizations. The majority of them believed artificial intelligence would change the world, and incidents and injuries will reduce by a huge percentage in the organization.
Ethical consideration
During the research, ethical concern remained to be top of the priority during the whole research. The study ensured the information provided was efficient and valid. The participants of the study were given a consent form to fill. The consent form also explained the purpose of the study, which was to find the Application of AI and Machine Learning in Safety Performance Management Systems and Measures. Anonymity and confidentiality were assured, and participants were informed they were not obliged to participate in the study, and they could withdraw at any time of their liking. The participants were informed that the findings would be published and stored in the library, but their names would not be kept private. The participants would be assigned letters to represent their original names.
Results and Discussion
This study was set out to investigate the Application of AI and Machine Learning in Safety Performance Management Systems and Measures. The findings show that AI and ML are crucial advancements in technology that will change many industries around the globe. Through research, we have discovered that AI is among the most successful technologies across different domains, such as medical application, transportation, education, and safety management systems. While AI can be integrated into many things, its application on the safety management system is the best. For instance, cars are now able to differentiate objects on the road. For example, Tesla Company has vehicles that have autopilot. The findings also show that not many people are aware of artificial intelligence and machine learning. People should be exposed to smart devices that are embedded with AI so that they can learn. Although AI has limits, especially in safety-critical systems, researchers should continue investing their time and money because this is for a good course. Programs should be created all over the world to support those working on AI projects.
Challenges & future research & conclusion
Challenges
One of the greatest challenges that AI needs to overcome is bias. Research shows that AI can be good and bad, depending on the data they are trained on. Bad data can be laced with gender, racial, ethnic, and communal biases. For example, an AI system can be biased in a job interview. AI systems use algorithms that make crucial decisions, and if these algorithms go unrecognized, then it could lead to unfair and unethical consequences (Amodei et al. 2016). When developing these AI systems, it is crucial to develop them using unbiased data. Especially when embedding them in a safety performance management system, it is important to use unbiased data. If the systems are not fed with unbiased data, it means they can cause incidents and injuries to individuals (Challen et al. 2019). The quality of data matters a lot when developing artificial intelligence. The massive datasets help artificial intelligence learn just like humans. System model errors is another challenge faced by Artificial intelligence. These errors lead to bad reasoning, which is a common mistake for AI systems. The challenge of bad reasoning should be solved to ensure the AI system is embedded in many fields for efficiency.
Future research
Experts say that AI will continue to advance in the next coming decade. Still, the majority of the people are very concerned about the advancement of Artificial intelligence will affect what it means to be productive as a human. Experts say that Artificial intelligence could disrupt human activities such as autonomy, capabilities, and agencies. Experts are now using neuroscience to accelerate Ai research in safety performance management systems. In the next couple of decades, AI machines will be able to do things for humans, especially dangerous situations. These will ensure incidents and injuries are reduced when people are in their workplaces.
Conclusion
The application of AI and Machine Learning in Safety Performance Management Systems and Measures will change the world significantly. Many people are always in danger, especially when they are at their workplaces. The study has analyzed and proved that AI and machine learning had made progress from science fiction to real-time usage in different industries. In the next few years, machines will be able to identify what is bad and good and make decisions based on data algorithms. The government should fund technology programs because utility and public safety agencies need AI for data mining and identifying ways of saving money and increasing efficiency in public services. In the next few years, there will be reduced physical injuries and work-related stress because of the application of AI and Machine Learning in Safety Performance Management Systems.
References
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Aziz, S., & Dowling, M. (2019). Machine Learning and AI for Risk Management. In Disrupting Finance (pp. 33-50). Palgrave Pivot, Cham.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.
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