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- W4313256260 abstract "Abstract With the perspective on applications of AI-technology, especially data intensive deep learning approaches, the need for methods to control and understand such models has been recognized and gave rise to a new research domain labeled explainable artificial intelligence (XAI). In this overview paper we give an interim appraisal of what has been achieved so far and where there are still gaps in the research. We take an interdisciplinary perspective to identify challenges on XAI research and point to open questions with respect to the quality of the explanations regarding faithfulness and consistency of explanations. On the other hand we see a need regarding the interaction between XAI and user to allow for adaptability to specific information needs and explanatory dialog for informed decision making as well as the possibility to correct models and explanations by interaction. This endeavor requires an integrated interdisciplinary perspective and rigorous approaches to empirical evaluation based on psychological, linguistic and even sociological theories." @default.
- W4313256260 created "2023-01-06" @default.
- W4313256260 creator A5027156356 @default.
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- W4313256260 date "2022-12-01" @default.
- W4313256260 modified "2023-10-14" @default.
- W4313256260 title "What is Missing in XAI So Far?" @default.
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- W4313256260 doi "https://doi.org/10.1007/s13218-022-00786-2" @default.
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