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pro vyhledávání: '"Palihawadana, Chamath"'
Autor:
Wijekoon, Anjana, Wiratunga, Nirmalie, Corsar, David, Martin, Kyle, Nkisi-Orji, Ikechukwu, Palihawadana, Chamath, Caro-Martínez, Marta, Díaz-Agudo, Belen, Bridge, Derek, Liret, Anne
Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Recent findings suggest that a single explainer may not meet the diverse needs of multiple users in an AI system; indeed, even individual u
Externí odkaz:
http://arxiv.org/abs/2408.12941
Autor:
Wiratunga, Nirmalie, Wijekoon, Anjana, Nkisi-Orji, Ikechukwu, Martin, Kyle, Palihawadana, Chamath, Corsar, David
Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies tha
Externí odkaz:
http://arxiv.org/abs/2109.05800
Autor:
Wiratunga, Nirmalie, Cooper, Kay, Wijekoon, Anjana, Palihawadana, Chamath, Mendham, Vanessa, Reiter, Ehud, Martin, Kyle
Delivery of digital behaviour change interventions which encourage physical activity has been tried in many forms. Most often interventions are delivered as text notifications, but these do not promote interaction. Advances in conversational AI have
Externí odkaz:
http://arxiv.org/abs/2004.14067
Publikováno v:
In Neurocomputing 28 April 2022 483:432-445
Autor:
Wijekoon, Anjana, Wiratunga, Nirmalie, Nkisi-Orji, Ikechukwu, Palihawadana, Chamath, Corsar, David, Martin, Kyle
Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are “actionable” explanations that help users to understand how model decisions can be changed by adapting features of an input. A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2659::42d1c235a8d5f68ed7facb74e3f051ad
https://zenodo.org/record/7473961
https://zenodo.org/record/7473961
Autor:
Wiratunga, Nirmalie, Wijekoon, Anjana, Nkisi-Orji, Ikechukwu, Martin, Kyle, Palihawadana, Chamath, Corsar, David
���Counterfactual explanations focus on ���actionable knowledge��� to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dfde2c99a2714e206f7a9442441c99f