Automated scoring systems provide multiple benefits but also pose challenges, notably potential bias. Various methods exist to evaluate these algorithms and their outputs for bias. Upon detecting bias, the next logical step is to investigate its cause, often by examining feature distributions. Recently, Johnson and McCaffrey proposed an exploratory approach to identify features responsible for differential prediction bias. However, their approach applies only to linear additive prediction models, excluding many machine learning algorithms. In this paper, we propose the bias contribution measure, a statistic that expands Johnson and McCaffrey's approach to any prediction algorithms that have partial derivatives and that can be implemented in any framework that supports automatic differentiation and matrix inversion. We demonstrated its application and effectiveness on synthetic and real-word data using multiple nonlinear prediction algorithms, including a single-layer feed-forward network (FFN), a support vector regressor, and a deep FFN with multiple hidden layers. In the synthetic data examples, the bias contribution measure successfully identified the feature responsible for the bias. When applied to a real-world data set, the bias contribution measure consistently identified the same set of features across all considered prediction algorithms.