5 research outputs found
Explanations of Black-Box Model Predictions by Contextual Importance and Utility
The significant advances in autonomous systems together with an immensely
wider application domain have increased the need for trustable intelligent
systems. Explainable artificial intelligence is gaining considerable attention
among researchers and developers to address this requirement. Although there is
an increasing number of works on interpretable and transparent machine learning
algorithms, they are mostly intended for the technical users. Explanations for
the end-user have been neglected in many usable and practical applications. In
this work, we present the Contextual Importance (CI) and Contextual Utility
(CU) concepts to extract explanations that are easily understandable by experts
as well as novice users. This method explains the prediction results without
transforming the model into an interpretable one. We present an example of
providing explanations for linear and non-linear models to demonstrate the
generalizability of the method. CI and CU are numerical values that can be
represented to the user in visuals and natural language form to justify actions
and explain reasoning for individual instances, situations, and contexts. We
show the utility of explanations in car selection example and Iris flower
classification by presenting complete (i.e. the causes of an individual
prediction) and contrastive explanation (i.e. contrasting instance against the
instance of interest). The experimental results show the feasibility and
validity of the provided explanation methods
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho