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Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 2019
The American College of Radiology (ACR) has guidelines on appropriate ordering of Magnetic Resonance Imaging (MRI) brain scans. MRI requests are currently manually reviewed by radiologists to ensure compliance to these guidelines. In this paper, we implemented a stacked recurrent neural network (RNN) utilizing a bidirectional long short-term memory (Bi-
Alwin Yaoxian Zhang +4 more
+4 more sources
The American College of Radiology (ACR) has guidelines on appropriate ordering of Magnetic Resonance Imaging (MRI) brain scans. MRI requests are currently manually reviewed by radiologists to ensure compliance to these guidelines. In this paper, we implemented a stacked recurrent neural network (RNN) utilizing a bidirectional long short-term memory (Bi-
Alwin Yaoxian Zhang +4 more
+4 more sources
2020
An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Panda, Swaroop, Roy, Shatarupa Thakurta
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An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Panda, Swaroop, Roy, Shatarupa Thakurta
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XRDS: Crossroads, The ACM Magazine for Students, 2019
How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
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How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
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Explainable for Trustworthy AI
2023Black-box Artificial Intelligence (AI) systems for automated decision making are often based on over (big) human data, map a user’s features into a class or a score without exposing why. This is problematic for the lack of transparency and possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training ...
Fosca Giannotti +2 more
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2020
Abstract Deep connectionist learning has resulted in very impressive accomplishments, but it is unclear how it achieves its results. A dilemma in using the output of machine learning is that the best performing methods are the least explainable. Explainable artificial intelligence seeks to develop systems that can explain their reasoning
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Abstract Deep connectionist learning has resulted in very impressive accomplishments, but it is unclear how it achieves its results. A dilemma in using the output of machine learning is that the best performing methods are the least explainable. Explainable artificial intelligence seeks to develop systems that can explain their reasoning
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Explaining Explanation For “Explainable Ai”
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2018What makes for an explanation of “black box” AI systems such as Deep Nets? We reviewed the pertinent literatures on explanation and derived key ideas. This set the stage for our empirical inquiries, which include conceptual cognitive modeling, the analysis of a corpus of cases of "naturalistic explanation" of computational systems, computational ...
Robert R. Hoffman +2 more
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SIGGRAPH Asia 2020 Courses, 2020
• Do Machine Learning algorithms have a Soul? • Could they understand every day's reality as us Humans do? • What the consequence of their Creativity? • Can they help us to understand world better?
Rowan Hughes +5 more
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• Do Machine Learning algorithms have a Soul? • Could they understand every day's reality as us Humans do? • What the consequence of their Creativity? • Can they help us to understand world better?
Rowan Hughes +5 more
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What does explainable AI explain?
2023Machine Learning (ML) models are increasingly used in industry, as well as in scientific research and social contexts. Unfortunately, ML models provide only partial solutions to real-world problems, focusing on predictive performance in static environments.
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Explainable AI (XAI): Explained
2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 2023G. Pradeep Reddy, Y. V. Pavan Kumar
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AI Matters, 2018
The power network is the largest operating machine on earth, generating more than US$400bn a year 1 keeping the lights on for our homes, offices, and factories. A significant concern in power networks is for the energy providers to be able to generate enough power to supply the demands at any
Ferdinando Fioretto, William Yeoh
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The power network is the largest operating machine on earth, generating more than US$400bn a year 1 keeping the lights on for our homes, offices, and factories. A significant concern in power networks is for the energy providers to be able to generate enough power to supply the demands at any
Ferdinando Fioretto, William Yeoh
openaire +1 more source