Employees working on construction projects have a higher risk of experiencing occupational accidents compared to workers in other sectors. Newly employed workers might face an occupational accident shortly after they start working due to not being able to notice risky environmental conditions. Despite existing studies in construction safety literature focusing on several output variables such as fatality, accident type or accident severity predictions, no studies have examined the short-term susceptibility of construction workers to occupational injuries. This study aims to develop a model to predict construction workers’ susceptibility to short-term occupational accidents using interpretable machine learning (ML) methods. Hence, the primary research objective is to identify construction workers who have high probability of experiencing an occupational accident shortly after their employment. In this respect, a national dataset of occupational accidents encountered in the construction industry in Turkey was collected and subjected to various pre-processing elements (data cleaning, data scaling, and data resampling) to prepare the data for prediction. At the processing step, Stochastic Gradient Boosting (SGB) algorithm was applied for the classification purpose. In the next step, Shapley Additive Explanations (SHAP) was used as an interpretable artificial intelligence algorithm to explain how, to what extent, and in which direction the input variables affect the prediction scheme, which is another distinguishing feature of the present study compared to past studies in the subject matter. Results show that the proposed SGB model is a powerful detector for the classification problem and salary of workers, past accident in the company, and number of workers in the company were the most influencing factors. Overall, this study contributes to practice by improving the safety conditions of the newly employed workers as well as minimizing their accident probability through intensified safety training. Given that contemporary safety management applications demand a new set of data-driven inputs, proposed model is expected to help industry professionals and safety managers apply more robust safety risk mitigation and/or prevention measures.