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This paper surveys visual methods of explainability of Machine Learning (ML)with focus on moving from quasi-explanations that dominate in ML todomain-specific explanation supported by granular visuals. ML interpretation isfundamentally a human activity and visual methods are more readilyinterpretable. While efficient visual representations of high-dimensional dataexist, the loss of interpretable information, occlusion, and clutter continueto be a challenge, which lead to quasi-explanations. We start with themotivation and the different definitions of explainability. The paper focuseson a clear distinction between quasi-explanations and domain specificexplanations, and between explainable and an actually explained ML model thatare critically important for the explainability domain. We discuss foundationsof interpretability, overview visual interpretability and present several typesof methods to visualize the ML models. Next, we present methods of visualdiscovery of ML models, with the focus on interpretable models, based on therecently introduced concept of General Line Coordinates (GLC). These methodstake the critical step of creating visual explanations that are not merelyquasi-explanations but are also domain specific visual explanations while thesemethods themselves are domain-agnostic. The paper includes results ontheoretical limits to preserve n-D distances in lower dimensions, based on theJohnson-Lindenstrauss lemma, point-to-point and point-to-graph GLC approaches,and real-world case studies. The paper also covers traditional visual methodsfor understanding ML models, which include deep learning and time seriesmodels. We show that many of these methods are quasi-explanations and needfurther enhancement to become domain specific explanations. We conclude withoutlining open problems and current research frontiers.

Survey of explainable machine learning with visual and granular methods beyond quasi-explanations