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AI Meets Incident Reporting
The use of artificial intelligence (AI) in the workplace usually invokes images of robots making human workers obsolete or, in more dystopian visions, keeping their unblinking machine eyes on the workforce in the manner of a hi-tech slave driver. At the same time, its current uses in the workplace are something taken for granted. Yet, AI is not here to bring about a bleak SF future for the workforce, but rather help workers stay healthy, happy and, most importantly, safe in their workplaces. Incident reporting is yet another area in which both the workers and managers will be actually thankful for having AI-powered systems watching over them and helping them make workplaces of the future better places for everyone.
AI: Simultaneously Proactive and Reactive
First of all, it is well known that occupational health and safety is an area most of the companies keep close tabs on. The reasons for this are multiple, ranging from the magnitude of the actual injuries and fatalities suffered by the workers to huge compensatory costs (nearly $62 billion USD for disabling, nonfatal workplace injuries for the U.S. workers only) the organizations have to bear in case a workplace accident is brought to the attention of the relevant judicial institutions. Thus, organizations of all types demonstrate readiness to invest in AI, primarily since they see it as a technological tool for the advanced management of the risks associated with work incidents.
In this sense, the role of this technology is twofold, as incident reporting has its preventive dimension in addition to what we perceive as a reactive one, meaning the manner in which we handle these events after they take place. Still, both of these segments have a common foundation in one thing: data, whose size and complexity in this area have historically proved to be a challenge even for the most diligent among the health and safety managers and supervisors.
Applying the Lessons Learned from Reports
The reason for this is the fact that incident data can be complex because they encompass descriptions of parameters such as the main cause(s) of injuries, incident severity, mechanisms that caused an injury, near-misses, safety observations and hazard analysis. When compiling data to accompany incident reports, managers are often faced with the obligation to get answers that involve tapping into pools of data that are both large, complex and unstructured. With its ability to process and sort out data at unprecedented speed, AI can serve as the source of answers for those who seek out fast access to information about a specific incident.
Designed with this in mind, AI will function as a platform which is plugged into these blocks of data, with an ability to offer quick answers in real time. Questions posed to it can be related to every piece of data on workplace health & safety, a bunch of which will deal with descriptions, not only of incidents themselves but of hazards, risks and near-miss situations.
Near-miss reporting can play a hugely important role in the prevention of the accidents and AI is the lynchpin of the efforts to reduce the related incident frequency rates. This is done not only for the health & safety compliance reasons but for the financial ones as well, with companies reducing their losses by as much as 90% after applying the lessons learned from the descriptions of near-miss incidents.
Drafting Data-Based Health and Safety Policies
Therefore, it is easy to see how the data-processing abilities of AI can actually deliver concrete financial goods to the table if the technology is made sophisticated enough to recognize and understand (in that order) the concepts of accidents, near-misses and risks.
A project of this type was recently implemented by the Business Finland agency in Finland, which employed natural language processing (NLP) technology of the AI to investigate all risk factors related to accidents which either took place or could be prevented. This involved sifting through copious amounts of textual data, including near-miss reports, which were both processed and analyzed by AI.
Once the results were up, they have shown that various accident types were reported more frequently for specific age groups. For example, younger employees were more likely to suffer accidents, compared to their older colleagues. Thus, AI was able not only to process a huge amount of accident-related data but also help the human operators identify essential trends to add, when drafting new occupational health & safety guidelines for a particular company or even industry.
Self-Evolving Compliance Officer
This is the area in which we are getting closer to the issue of how the reports on previous accidents can help make AI 'smarter' and more useful in preventing accidents from happening in the first place. Machine learning will play a key role here, as it allows AI to arrive at new insights based on its interaction with the human operators. Thus, the ability to use natural language to pose questions related to collected incident and safety data will, in turn, make AI more capable of giving responses based on which the managers can design advanced safety practices at the organizational level.
Companies such as Microsoft are already working on AI-powered software which will prevent workplace incidents based on constant real-time supervision and identification of risks based on previously recorded accidents. Similarly, aircraft industry players already work on implementing AI-based algorithms which will analyze photographic evidence and written accounts of the previous incidents to identify patterns and propose solutions to current issues.
The reason for the superiority of AI in this case is simple, as the benefits of the learning process involving the studying of accident reports are often restricted to a single person or a team in charge of this task. At the same time, the outcomes of this learning process are sometimes left undocumented, so whatever is learned about the categorization of incidents, for instance, remains confined to an operator in question.
In contrast, the AI machine learning makes this process both continuous and constantly improving, as this technology is able to learn from its earlier successful undertakings. In this manner, AI acts as a sort of constantly evolving health & safety compliance officer that collects data from the incident reports, organized databases on it, identifies trends and makes it easier for the company to eventually adopt the best possible health and safety practices, including those related to the incident reporting.
Instead of acting as a job-destroying bugbear, AI's role in modern-day workplace will revolve around helping managers and workers approach incident reporting and health & safety policies in a strategic and analytical manner. With its ability to process terabytes of data in a span of a few seconds, AI is capable of making incident report analysis a backbone of new strategies aimed at making this process more streamlined and data-based. In addition, its machine learning capability allows AI to go beyond the human operator’s limitations in utilizing the collected data to improve both either their work performance or incident reporting and managerial policies at the organizational level. Finally, the role of AI in this regard is not limited to post-accident management, since it is capable of using an ever-increasing amount of data it processes to become a powerful tool