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Explainable AI (XAI)
The new generation of AIArtificial Intelligence (AI) technology should enable creation of explainable systems that usersUser can understand. Although...
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Is explainable AI responsible AI?
When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that...
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Explainable AI in the military domain
Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and...
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Exploring explainable AI in the tax domain
This paper analyses whether current explainable AI (XAI) techniques can help to address taxpayer concerns about the use of AI in taxation. As tax...
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Explainable AI and Law: An Evidential Survey
Decisions made by legal adjudicators and administrative decision-makers often found upon a reservoir of stored experiences, from which is drawn a...
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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers
Artificial intelligence technology is widely used in the field of wireless sensor networks (WSN). Due to its inexplicability, the interference...
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Explainable AI: To Reveal the Logic of Black-Box Models
Artificial intelligence (AI) is continuously evolving; however, in the last 10 years, it has gotten considerably more difficult to explain AI models....
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Attitudinal Tensions in the Joint Pursuit of Explainable and Trusted AI
It is frequently demanded that AI-based Decision Support Tools (AI-DSTs) ought to be both explainable to, and trusted by, those who use them. The...
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Exploring explainable AI methods for bird sound-based species recognition systems
To recognize birds based on their calls, it would be helpful to have access to a machine-learning system. Researchers use machine learning and...
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Survey on Explainable AI: From Approaches, Limitations and Applications Aspects
In recent years, artificial intelligence (AI) technology has been used in most if not all domains and has greatly benefited our lives. While AI can...
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Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI
Generative AI models for music and the arts in general are increasingly complex and hard to understand. The field of explainable AI (XAI) seeks to...
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Map** the landscape of ethical considerations in explainable AI research
With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical...
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Explainable for Trustworthy AI
Black-box Artificial Intelligence (AI) systems for automated decision making are often based on over (big) human data, map a user’s features into a...
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AI employment decision-making: integrating the equal opportunity merit principle and explainable AI
Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews...
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How Explainable Is Explainability? Towards Better Metrics for Explainable AI
Despite the fact that machine learning has been applied in innumerable domains, its models have usually operated in a black box fashion, i.e. without...
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Analyzing and assessing explainable AI models for smart agriculture environments
We analyze a case study in the field of smart agriculture exploiting Explainable AI (XAI) approach, a field of study that aims to provide...
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Unveiling hidden factors: explainable AI for feature boosting in speech emotion recognition
Speech emotion recognition (SER) has gained significant attention due to its several application fields, such as mental health, education, and...
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An object-oriented neural representation and its implication towards explainable AI
Rapid dissemination of Artificial Intelligence (AI) and machine learning in real-world problems has raised a concern about the reliability aspects of...
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BiGRU-ANN based hybrid architecture for intensified classification tasks with explainable AI
Artificial Intelligence (AI) is increasingly being employed in critical decision-making processes such as medical diagnosis, credit approval,...
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Creating meaningful work in the age of AI: explainable AI, explainability, and why it matters to organizational designers
In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences...
