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- W4287121490 abstract "Local surrogate approaches for explaining machine learning model predictions have appealing properties, such as being model-agnostic and flexible in their modelling. Several methods exist that fit this description and share this goal. However, despite their shared overall procedure, they set out different objectives, extract different information from the black-box, and consequently produce diverse explanations, that are -- in general -- incomparable. In this work we review the similarities and differences amongst multiple methods, with a particular focus on what information they extract from the model, as this has large impact on the output: the explanation. We discuss the implications of the lack of agreement, and clarity, amongst the methods' objectives on the research and practice of explainability." @default.
- W4287121490 created "2022-07-25" @default.
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- W4287121490 date "2021-06-10" @default.
- W4287121490 modified "2023-09-23" @default.
- W4287121490 title "On the overlooked issue of defining explanation objectives for local-surrogate explainers" @default.
- W4287121490 doi "https://doi.org/10.48550/arxiv.2106.05810" @default.
- W4287121490 hasPublicationYear "2021" @default.
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