More transparent and explainable machine learning algorithms are required to provide enhanced and sustainable dataset understanding

For detailed dataset interrogation and auditing purposes the lack of dataset explainability/transparency of the majority of available machine-learning (ML) models poses limitations. There is a tendency for ML models to focus on prediction speed and accuracy at the expense of transparently revealing dataset relationships. A case is made here to broaden that focus and for ML models to offer alternative configurations tailored to provide more explanations about how individual predictions are derived. Indeed, those striving to achieve sustainable objectives should not rely on opaque ML models and seek transparency as a fundamental objective of good modelling practice (GMP). Doing so tends to boost trust and confidence in the outputs of models relating to complex socioenvironmental systems (SES), particularly those being used to potentially justify controversial social, political and ethical decisions. Currently, the transparent open box algorithms (TOB) are the only ML algorithms available that are configured specifically to routinely provide detailed data record relationships for each of their predictions. This study describes the data mining benefits of the Python-coded optimized data-matching TOB algorithms generally, and when applied to environmental datasets characterized by complex non-linear relationships involving many variables.