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- W3085905482 abstract "This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. These dimensions enable a descriptive characterization, facilitating comparisons between different study designs. They further structure the design space of XAI, converging towards a precise terminology required for a rigorous study of XAI. Our literature review differentiates between comparative studies and application papers, revealing methodological differences between the fields of machine learning, human-computer interaction, and visual analytics. Generally, each of these disciplines targets specific parts of the XAI process. Bridging the resulting gaps enables a holistic evaluation of XAI in real-world scenarios, as proposed by our conceptual model characterizing bias sources and trust-building. Furthermore, we identify and discuss the potential for future work based on observed research gaps that should lead to better coverage of the proposed model." @default.
- W3085905482 created "2020-09-21" @default.
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- W3085905482 date "2020-09-14" @default.
- W3085905482 modified "2023-09-27" @default.
- W3085905482 title "Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations." @default.
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