Comparing Case-based Classification Models with Interpretable Machine Learning Classification Models - a study based on human moral judgement in the bio-ethical domain

dc.rights.license	CC-BY-NC-ND
dc.contributor.advisor	Prakken, Henry
dc.contributor.author	Bogaards, Ellen
dc.date.accessioned	2023-07-20T00:02:12Z
dc.date.available	2023-07-20T00:02:12Z
dc.date.issued	2023
dc.identifier.uri	https://studenttheses.uu.nl/handle/20.500.12932/44222
dc.description.sponsorship	Utrecht University
dc.language.iso	EN
dc.subject	Both Canavotto & Horty (2022) and Sinnott-Armstrong & Skorburg (2021) have discussed a promising approach that can be used to determine how people use information on morally relevant features for their moral judgements on kidney allocation dilemmas. This research has developed, applied and compared an interpretable ML approach, inspired by the approach discussed in Sinnott-Armstrong & Skorburg (2021), and a case-based approach that integrates the result model of constraint, inspired by the appro
dc.title	Comparing Case-based Classification Models with Interpretable Machine Learning Classification Models - a study based on human moral judgement in the bio-ethical domain
dc.type.content	Master Thesis
dc.rights.accessrights	Open Access
dc.subject.keywords	moral judgement, case-based classification, interpretable machine learning
dc.subject.courseuu	Artificial Intelligence
dc.thesis.id	19496