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A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
IEEE Access, 2023 The advancement of Artificial Intelligence (AI) technology has accelerated the development of several systems that are elicited from it. This boom has made the systems vulnerable to security attacks and allows considerable bias in order to handle errors ...
Vinay Chamola +5 more
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Explainable AI: A Neurally-Inspired Decision Stack Framework
Biomimetics, 2022 European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data.
Muhammad Salar Khan +5 more
doaj +1 more source
npj Digital Medicine, 2023 White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability.
Zehua Dong +20 more
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Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps [PDF]
, 2020 With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation.
Amir, Ofra +3 more
core +2 more sources
Designing and Evaluating User Experience of an AI-Based Defense System
IEEE Access, 2023 In recent years, artificial intelligence (AI) has been applied in various fields, with rapid expansion of the scope of AI-human interactions. However, most AI technologies continue to exhibit black-box characteristics, i.e., their decisions and actions ...
Sunyoung Park +3 more
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A mental models approach for defining explainable artificial intelligence
BMC Medical Informatics and Decision Making, 2021 Background Wide-ranging concerns exist regarding the use of black-box modelling methods in sensitive contexts such as healthcare. Despite performance gains and hype, uptake of artificial intelligence (AI) is hindered by these concerns.
Michael Merry, Pat Riddle, Jim Warren
doaj +1 more source
WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis
Frontiers in Physiology, 2022 IntroductionRepresentation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream tasks.
Rob Brisk +8 more
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Proceedings of the 25th International Conference on Intelligent User Interfaces, 2020 Explainable AI (XAI) has started experiencing explosive growth, echoing the explosive growth that has preceded it of AI becoming used for practical purposes that impact the general public. This spread of AI into the world outside of research labs brings with it pressures and requirements that many of us have perhaps not thought about deeply enough.
openaire +1 more source
Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting
Machine Learning and Knowledge Extraction, 2023 The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI ...
Federico Cabitza +5 more
doaj +1 more source
From ”Explainable AI” to ”Graspable AI”
Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction, 2021 Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s ...
Ghajargar, Maliheh +7 more
openaire +6 more sources