Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations

• URL: http://arxiv.org/abs/2009.06433v1
• Date: Mon, 14 Sep 2020 13:40:51 GMT
• Title: Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations
• Authors: Fabian Sperrle, Mennatallah El-Assady, Grace Guo, Duen Horng Chau, Alex Endert, Daniel Keim
• Abstract summary: This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. They 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.
• Score: 19.68184991543289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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