Safety Data Analysis with Machine Learning

Abstract:
Safety professionals receive data from internal and external sources, then manually determine whether the issue constitutes a safety hazard. Many reports are received, and each report is reviewed, then investigated further, using a tedious, labor intensive, and possibly error prone process. In the course of reaching a decision, human bias is inevitable - any two humans could reach different conclusions, and the same individual human could draw different conclusions on different days. As technology has advanced, numerous approaches have been pursued, attempting to reduce human bias and improve both efficiency and effectiveness of the process. In recent years, moderate success was achieved, which provided accuracy rates near 85% but continued refinement did not achieve acceptable results. In early 2023, the challenge was given to a new team, and within a few months, state-of-the-art Artificial Intelligence/Machine Learning data analytics techniques were utilized to aid in safety data analysis efforts, which resulted in high accuracy and efficiency, with reduced human bias.

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