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Machine-learning (ML) models — compared to strictly additive models — can provide notable predictive lift when data presents complex relationships. However, without an understanding of the relationships captured by the ML model, we risk encoding accidental, unintentional and even undesirable features into these predictions. These surprising relationships may be introduced by unexpected biases in our data- collection methods, or by confounding treatments in our historical practices, which, if undetected, could yield models that are unfit for their intended tasks. On the bright side, however, revelations from an ML model’s content can inspire greater insights for the model creators. They may also foster greater trust among its users. This paper seeks to explore, illustrate and compare Explainable Artificial Intelligence (xAI) techniques that can help us gain deeper insights from ML models and operationalize them with far greater confidence. Specifically, we outline some of the explainability support for machine learning provided by toolsets available from FICO.
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