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Tailings dams, critical for storing mine waste and water, must maintain stability and functionality throughout their lifespan. Their design and risk assessment are complicated by significant uncertainties stemming from multivariable parameters, including material properties, loading conditions, and operational decisions. Traditional dam design and risk assessment procedures often rely on first-order probabilistic approaches, which fail to capture the complex, multi-layered nature of these uncertainties fully. This paper reviews the current tailings dam design practice and proposes the application of Bayesian networks (BNs) to analyse the epistemic and aleatory uncertainty inherent in tailings dam design parameters and risk assessment. By representing these uncertainties explicitly, BNs can facilitate more robust and targeted design strategies. The proposed approach involves several key steps: • Parameterisation: Design model input parameters are represented individually and defined by their respective probability distribution functions. This separates the various sources of uncertainty, promoting a more granular analysis. • Knowledge Elicitation: Probability fundamentals are established by eliciting expert opinions, which are then grounded in scientific evidence. This formalises subjective knowledge and incorporates it into the quantitative model. • Model Assessment and Integration: Models are assessed to deduce parameter distributions. The final BN model integrates these distributions with hypotheses and conditional probabilities to model the chain of events that could lead to various failure mechanisms and how these can be deduced with BNs, providing a more dynamic approach for risk analysis. This methodology provides a sophisticated and comprehensive approach to accounting for the full spectrum of uncertainties, thereby enhancing the reliability of tailings dam designs and risk management decisions.