Assessing and Screening WHS Regulatory Risk using a Machine Learning Model
WHS Partitioners/Project Leaders: who desire to have a more detailed understanding of predictive modelling and the potential to apply A.I. in the WHS domain. WHS Data Analysts: who create WHS dashboard in a regular basis to gain a better understanding of how to collect, clean and analyse data that fully support future predictive modelling application.
Predictive modelling has been used extensively in the insurance and finance industries. The power of applying machine learning techniques in the WHS domain, however has not been fully unleashed. By integrating the computational power of A.I. and the insights and knowledge from WHS practitioners, organisations and regulators can make data-driven and evidence-informed decisions to ensure that risk-mitigation interventions are appropriately directed towards high-risk populations and activities.