Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects

Machine learning (ML) has increased greatly in both popularity and significance, driven by an increase in methods, computing power and data availability [33]. On July 5, 2024, a search on Web of Science for publications including the term "machine learning" yielded more than 350,000 results, corresponding to an average annual increase by more than 20% since 2006. ML models are often characterized by their high generalizability, making them particularly successful when used for supervised learning tasks like classification and risk prediction. In recent years, ML models based on deep artificial neural networks (ANNs) have led to groundbreaking results in the development of high-performing prediction models. The high prediction accuracy of modern ML models is usually achieved by optimizing complex "black-box" architectures with thousands of parameters. As a consequence, they often result in predictions that are difficult, if not impossible, to interpret. This interpretability problem has been hindering the use of ML in fields like medicine, ecology and insurance, where an understanding of the model and its inner workings is paramount to ensure user acceptance and fairness. In a recent environmental study, for example, we explored the use of ML to derive predictions of stream biological condition in the Chesapeake Bay watershed of the mid-Atlantic coast of North America [26]. Clearly, if these predictions are intended to inform future management policies (projecting, e.g., changes in land use, climate and watershed characteristics), they are required to be interpretable in terms of relevant features as well as the directions and strengths of the feature effects.