Searched for: subject:"Explainable%5C+AI"
(1 - 9 of 9)
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Schoonderwoerd, T.A.J. (author), Jorritsma, W. (author), Neerincx, M.A. (author), van den Bosch, K. (author)
Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated...
article 2021
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Verhagen, R.S. (author), Neerincx, M.A. (author), Tielman, M.L. (author)
Because of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more...
conference paper 2021
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de Greeff, J. (author), de Boer, M.H.T. (author), Hillerström, F.H.J. (author), Bomhoff, F.W. (author), Jorritsma, W. (author), Neerinx, M. (author)
AI tools are becoming more commonly used in a variety of application domains. In this paper, we describe a system named FATE that combines state of the art AI tools. The goal of the FATE system is decision support with use of ongoing human-AI co-learning, explainable AI and fair, bias-free and secure usage of data. These topics are societally...
conference paper 2021
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van der Waa, J. (author), Verdult, S. (author), van den Bosch, K. (author), van Diggelen, J. (author), Haije, T. (author), van der Stigchel, B. (author), Cocu, I. (author)
With the progress of Artificial Intelligence, intelligent agents are increasingly being deployed in tasks for which ethical guidelines and moral values apply. As artificial agents do not have a legal position, humans should be held accountable if actions do not comply, implying humans need to exercise control. This is often labeled as Meaningful...
article 2021
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Waa, J.V.D. (author), Schoonderwoerd, T. (author), Diggelen, J.V. (author), Neerincx, M. (author)
Decision support systems (DSS) have improved significantly but are more complex due to recent advances in Artificial Intelligence. Current XAI methods generate explanations on model behaviour to facilitate a user's understanding, which incites trust in the DSS. However, little focus has been on the development of methods that establish and...
article 2020
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Sileno, G. (author), Boer, A. (author), van Engers, T. (author)
For being potentially destructive, in practice incomprehensible and for the most unintelligible, contemporary technology is setting high challenges on our society. New conception methods are urgently required. Reorganizing ideas and discussions presented in AI and related fields, this position paper aims to highlight the importance of normware...
conference paper 2019
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Adhikari, A. (author), Tax, D.M.J. (author), Satta, R. (author), Faeth, M. (author)
Explainable Artificial Intelligence (XAI) is an emergent research field which tries to cope with the lack of transparency of AI systems, by providing human understandable explanations for the underlying Machine Learning models. This work presents a new explanation extraction method called LEAFAGE. Explanations are provided both in terms of...
conference paper 2019
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van den Bosch, K. (author), Schoonderwoerd, T.A.J. (author), Blankendaal, R.A.M. (author), Neerincx, M.A. (author)
The increasing use of ever-smarter AI-technology is changing the way individuals and teams learn and perform their tasks. In hybrid teams, people collaborate with artificially intelligent partners. To utilize the different strengths and weaknesses of human and artificial intelligence, a hybrid team should be designed upon the principles that...
conference paper 2019
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Neerincx, M.A. (author), van der Waa, J. (author), Kaptein, F. (author), van Diggelen, J. (author)
conference paper 2018
Searched for: subject:"Explainable%5C+AI"
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