What if we could recognize danger before an accident occurs? Anyone who has ever thought about safety has probably wished for this at least once. But risks at industrial sites vary by scale, equipment, work methods, and worker composition, even within the same industry. There are limits to relying solely on human experience to decide which of countless workplaces to examine first. This is where artificial intelligence (AI) reveals new possibilities. The Korea Occupational Safety and Health Agency has developed a "high-risk workplace prediction AI model" and is using it in accident prevention programs. The approach combines a workplace's industry, scale, equipment, and inspection history to first identify sites with a high likelihood of accidents. Clues that remain invisible when scattered emerge as signals of danger once the data is gathered.
The significance of this change lies in a shift in the safety management paradigm. In the past, the emphasis was on finding causes after an accident occurred and preventing recurrence. Now, sites with a high likelihood of accidents can be visited, inspected, and supported first. Limited personnel and budgets can also be concentrated first on more dangerous sites. In fact, the agency selected and intensively managed "2,000 ultra-high-risk workplaces" to reduce accidents that recur repeatedly at industrial sites, such as falls and being caught in machinery. At the selected workplaces, agency staff visit in person to examine the work sites, jointly identify major risk factors, and provide technical support that guides improvement measures. Rather than merely picking out dangerous sites, the support extends to technical assistance so that sites can actually change. The safety and health data the agency has accumulated over a long period has met AI and begun to serve as a guide for prevention.
Of course, AI does not provide all the answers on its own. Risk signals alone make it difficult to know what needs to be changed. What is truly needed is specific guidance on what is dangerous, where to start fixing things, and which improvement methods fit our own conditions.
The agency plans to further advance its prediction model going forward, pointing out risk factors for each workplace and linking them to necessary safety and health information, improvement measures, educational content, and support programs. This means creating AI that leads to on-site practice, not AI that ends with prediction. It can be a great help to small workplaces that lack dedicated safety and health personnel.
Changes in safety management using AI spread more widely when sites understand them, companies participate, and the public feels their effects. To this end, the Ministry of Employment and Labor and the agency are holding a "Month of Industrial Safety and Health" event this month. The event will present a variety of programs centered on an "AI Safety and Health Expo." A "Zone for the AI Industrial Safety New Technology Competition," immersive virtual reality (VR) safety experiences, and seminars examining AI technology trends and domestic and international cases of industrial safety and health applications are being prepared. We hope it will be a place for industrial site officials to find new solutions and an opportunity for the general public to feel safety closer to them.
The goal of safety is always the same: that people who work return home safely without being injured. What must change is the method of reaching that goal. AI is not a technology that replaces people but a tool that first alerts them to risks people might miss. When we read risks ahead of time through data and connect those signals to on-site improvements, accident prevention becomes practice before an accident, not cleanup after one. Safety that predicts and prevents accidents. That is the new path industrial safety and health must take in the era of AI transformation.
