Copyright © 2020 Elsevier B.V. or its licensors or contributors. Eschewing observations of incorrect decisions means a user will never be able to identify when she should not be confident, and hence not rely on a DNN. The field guide: i) Discusses the traits of Explainable Deep Learning for Analysing Brain Data Abstract: In this short abstract I will discuss recent directions where deep learning is used for analysing brain imaging data, both in the context of BCI and fMRI - summarizing steps taken by the BBCI team and co-workers. A central tenet in explainable machine learning is that the algorithm must emit information allowing a user to relate characteristics of input features with its output. (ii) given cues from its input, guard against choices that can negatively impact the user or society; legislation, law enforcement, etc. A Machine-centric Strategy to share, Deep learning methods have been very effective for a variety of medical becomes entangled and compressed into a single value via a non-linear transform of a weighted sum of feature values. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. field. Computer Methods and Programs in Biomedicine, https://doi.org/10.1016/j.cmpb.2020.105608. Safety. 0 The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. This space summarizes the core aspects of explainable DNN techniques that a majority of present work is inspired by or built from (Section LABEL:section:methods). Topics that are complementary to explainability may involve the inspection of a DNN’s weights or activations, the development of mechanisms that mathematically explain how DNNs learn to generalize, or approaches to reduce a DNN’s sensitivity to particular input features. deployment of the decision support system and that the decisions made are share, There has been a significant surge of interest recently around the conce... We propose a novel categorization scheme to systematically organize numerous existing methods on explainable deep learning, depicting the field in a clear and straightforward way. (see Section LABEL:subsection:fairness_and_bias). 23 Traits, therefore, represent a particular objective or an evaluation criterion for explainable deep learning systems. 0 Observing how the actions of a trained agent in a physical environment mimic the actions a human would give some confidence that its action choice calculus is aligned with a rational human. 0 Hence, it is exceedingly difficult to trace how particular stimulus properties drive this decision. the form of a rationale often conforms to a researcher’s personal notion of what constitutes an “explanation”. share. 04/07/2018 ∙ by Jaegul Choo, et al. This allows users to individually assess if the reasoning of a DNN is compatible with the moral principles it should operate over. Confidence grows when the “rationale” of a DNN’s decision is congruent with the thought process of a user. achieving human-level performance on many learning tasks. Given the existing reviews, the contributions of our article are as follows. DNN to be able to intuitively answer the question: When does this DNN work or not work? Of course, a DNN’s output is based on a deterministic computation, rather than a logical rationale. share. Explainability research allowing users to evaluate these observations (through an interpretation of activations during a DNN’s forward pass, for example) is one avenue for enhancing trust. technology is being developed, the development of new methods and studies on We show how deep learning can be applied for COVID-19 detection from chest X-rays; • The proposed method is aimed to mark as first step a chest X-ray as related to a healthy patient or to a patient with pulmonary diseases, the second step is aimed to discriminate between generic pulmonary disease and COVID-19. The right way to evaluate if a DNN is acting ethically is a topic of debate. Therefore, the best way to evaluate trust is with system observations (spanning both output and internal processing) over time. embarking into the field of explainable deep learning. This “field guide” is designed to help an uninitiated researcher understand: Traits can be thought of as a simple set of qualitative target contributions that the explainable DNN field tries to achieve in the results. DNNs. GitHub Gist: instantly share code, notes, and snippets. Such a review would help the uninitiated researchers thoroughly understand the connections between the related fields and explainable deep learning, and how jointly those fields help shape the future of deep learning towards transparency, robustness, and reliability. The last step is aimed to detect the interesting area in the chest X-ray (to provide explainability); We propose an explainable method aimed to automatically detect the areas of interest in the chest X-ray, symptomatic of the COVID-19 disease. This diversity is further compounded by the fact that This paper develops an explainable deep learning model that estimates th... how can one who will be held accountable for a decision trust a DNNs recommendation, and justify its use? We provide a detailed field guide for researchers who are uninitiated in explainable deep learning, aiming to lower or even eliminate the bar for them to come into this field. Complementary DNN topics are reviewed and the relationships between explainable DNNs and other related research areas are developed. share, This paper develops an explainable deep learning model that estimates th... The fourth aspect gives necessary feedback to the user to assess safety. ∙ The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. 37 0 post-explaina... ∙ To alleviate this Some argue that many successful applications of DNNs do not require explanations debate2017, A set of dimensions that characterize the space of work that constitutes foundational work in explainable deep learning, and a description of such methods. For example, saliency maps of attention mechanisms on

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