A Survey on the Explainability of Supervised Machine Learning
It addresses the need for transparency and accountability in AI decision-making for practitioners and researchers, but is incremental as it synthesizes existing work.
This survey paper tackles the problem of understanding black-box supervised machine learning models, particularly in sensitive areas like healthcare and finance, by providing definitions, reviewing past and recent explainable approaches, and classifying them with a case study.
Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or fifinance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.