I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI
| Autor: | Andreas Butz, Malin Eiband, Adrian Krüger, Felicitas Buchner, Michael Chromik |
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| Rok vydání: | 2021 |
| Předmět: |
Point (typography)
Interface (Java) Computer science Unintended consequences media_common.quotation_subject 05 social sciences Illusion 02 engineering and technology Cognitive bias 020204 information systems Perception 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Construct (philosophy) 050107 human factors media_common Cognitive psychology Interpretability |
| Zdroj: | IUI |
| Popis: | Unintended consequences of deployed AI systems fueled the call for more interpretability in AI systems. Often explainable AI (XAI) systems provide users with simplifying local explanations for individual predictions but leave it up to them to construct a global understanding of the model behavior. In this work, we examine if non-technical users of XAI fall for an illusion of explanatory depth when interpreting additive local explanations. We applied a mixed methods approach consisting of a moderated study with 40 participants and an unmoderated study with 107 crowd workers using a spreadsheet-like explanation interface based on the SHAP framework. We observed what non-technical users do to form their mental models of global AI model behavior from local explanations and how their perception of understanding decreases when it is examined. |
| Databáze: | OpenAIRE |
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