Note that a prediction with high probability does not guarantee its trustworthiness, as shown in recent adversarial studies goodfellow2014explaining,nguyen2015deep,moosavi2016deepfool,yuan2019adversarial. The aims of the field are established by understanding the traits desired in explainable DNNs. Hence, it is exceedingly difficult to trace how particular stimulus properties drive this decision. embarking into the field of explainable deep learning. 0 (ii) given cues from its input, guard against choices that can negatively impact the user or society; applicants zhao2018employee,qin2018enhancing. The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. . Finally, the overview of our current limitations and seldom-looked at aspects of explainable deep learning suggests new research directions. ∙ to the wide-spread adoption of DNNs we are beginning to see in society. ∙ 10/16/2019 ∙ by Zhong Qiu Lin, et al. places explainability in the context of other related deep learning research ... The limited information captured in a DNN’s parameters associated with input features ∙ More importantly, it is desirable if this system can give a rationale that both physicians and patients can understand and trust. high-level decisions can be sure that the DNN decisions are driven by ∙ Wright State University 07/17/2019 ∙ by Erico Tjoa, et al. People analytics and human resource platforms now tout the ability to predict employee performance and time to resignation, and to automatically scan the CV of job 10/16/2019 ∙ by Zhong Qiu Lin, et al. This allows users to individually assess if the reasoning of a DNN is compatible with the moral principles it should operate over. Such decision support systems can be found in critical domains, such as post-explaina... Thus, rather than making DNNs inherently ethical, this trait can be expressed by some notion of an “ethics code” that the system’s decisions are formed under. A Machine-centric Strategy to But there are other ways to think about this term: Explainable could. The second one to discern between COVID-19 and pneumonia. The first aspect of safety aligns this trait with trust since trust in a system is a prerequisite to consider it safe to use. DNN-powered facial recognition systems, for example, are now associating people with locations and activities under wide-spread surveillance activities with opaque intent masi2018deep. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. ∙ It is desirable if this system could also provide a rationale behind its predictions. Results and Conclusion: Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97. 04/07/2018 ∙ by Jaegul Choo, et al. Achieving trust and finding justification in a DNN’s recommendation can hardly be achieved if the user does not have access to a satisfactory explanation for the process that led to its output. Assume that the system makes life-altering predictions about whether or not a patient has a terminal illness. ∙ Confidence. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. and in these instances, enforcing constraints that provide explainability may hamper performance. Due to the incredible pace at which DNN Given the existing reviews, the contributions of our article are as follows. 04/30/2020 ∙ by Ning Xie, et al. For example, different users assume their own unique code of ethics, ethical decisions in one culture may be unethical in another, and there may be instances where no possible decision is consistent with a set of moral principles. Our article is specifically targeting on deep learning explanation while existing reviews either focus on explanations of general artificial intelligence methods or are less as detailed and comprehensive as ours. share. The field of ethical decision making in AI is growing as a field in and of itself 0 image park2018multimodal,hudson2018compositional or text vaswani2017attention,luong2015effective,letarte2018importance,he2018effective inputs reassure a user that the same semantically meaningful parts of the input she would focus on to make a classification decision are being used. It is important that the humans making DNN to be able to intuitively answer the question: When does this DNN work or not work? explaining the decision-making process of DNNs has blossomed into an active Authors: Ning Xie, Gabrielle Ras, Marcel van Gerven, Derek Doran. Radboud Universiteit Complementary research topics that are aligned with explainability. and some on deep learning ras2018explanation,montavon2018methods,zhang2018visual,samek2017explainable,erhan2010understanding. share, There has been a significant surge of interest recently around the conce... ∙ This allows the user to verify the rationality of the decision making process with respect to the environment that the system is operating in. designed as an easy-to-digest starting point for those just embarking in the for those uninitiated in the field. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Deep neural network (DNN) is an indispensable machine learning tool for (iv) provide feedback to a user about how operating conditions influence its decisions. 37 (see Section LABEL:subsection:fairness_and_bias). research field. Join one of the world's largest A.I. Yet, due to its We can say that a DNN promotes explainability if the system exhibits any trait that is justifiably related to explainability. Others claim that, because DNNs are inherently not explainable, an ascribed explanation is at best a plausible story about how the network processes an input that cannot be proven rudin2019stop. This diversity is further compounded by the fact that ∙ ∙ combinations of data features that are appropriate in the context of the The field guide then turns to essential considerations that need to be made when building an explainable DNN system in practice (which could include multiple forms of explanation to meet requirements for both users and DNN technicians). By continuing you agree to the use of cookies. The feedback may include an evaluation of its environment, the decision reached, and how the environment and the input data influence the decision made. Of course, a DNN’s output is based on a deterministic computation, rather than a logical rationale. The definition of safety is multi-faceted. A trait represents a property of a DNN necessary for a user to evaluate its output lipton2018mythos. 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). Analysing biomedical imaging the patient shows signs of pneumonia. Users must be able to use their confidence to measure the operational boundaries of a 04/28/2020 ∙ by Namkyoung Lee, et al. Recently, artificial intelligence, especially machine learning has DNNs. A review of related topics that are closely related to the realm of explainable deep learning is elaborated. It is thus worth noting that DNNs are not inherently “explainable”. DNNs whose decisions (in)directly lead to an event impacting human life, wealth, or societal policy should be safe. (iii) exhibit high reliability under both standard and exceptional operating conditions; Traits, therefore, represent a particular objective or an evaluation criterion for explainable deep learning systems. 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.
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