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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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Designing Theory-Driven User-Centric Explainable AI
International Conference on Human Factors in Computing Systems, 2019From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI).
Danding Wang +3 more
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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.
openaire +1 more source
Healthcare
Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning.
Qaiser Abbas, Woonyoung Jeong, S. Lee
semanticscholar +1 more source
Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning.
Qaiser Abbas, Woonyoung Jeong, S. Lee
semanticscholar +1 more source
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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LLMs for Explainable AI: A Comprehensive Survey
arXiv.orgLarge Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping bridge the gap ...
Ahsan Bilal, David S. Ebert, Beiyu Lin
semanticscholar +1 more source
Journal of Risk and Financial Management
The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties.
Saif Khalifa Aljunaid +3 more
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The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties.
Saif Khalifa Aljunaid +3 more
semanticscholar +1 more source
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
openaire +1 more source