Measuring User Understanding in Dialogue-based XAI Systems

• URL: http://arxiv.org/abs/2408.06960v2
• Date: Wed, 14 Aug 2024 12:11:12 GMT
• Title: Measuring User Understanding in Dialogue-based XAI Systems
• Authors: Dimitry Mindlin, Amelie Sophie Robrecht, Michael Morasch, Philipp Cimiano,
• Abstract summary: State-of-the-art in XAI is still characterized by one-shot, non-personalized and one-way explanations. In this paper, we measure understanding of users in three phases by asking them to simulate the predictions of the model they are learning about. We analyze the data to reveal patterns of how the interaction between groups with high vs. low understanding gain differ.
• Score: 2.4124106640519667
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The field of eXplainable Artificial Intelligence (XAI) is increasingly recognizing the need to personalize and/or interactively adapt the explanation to better reflect users' explanation needs. While dialogue-based approaches to XAI have been proposed recently, the state-of-the-art in XAI is still characterized by what we call one-shot, non-personalized and one-way explanations. In contrast, dialogue-based systems that can adapt explanations through interaction with a user promise to be superior to GUI-based or dashboard explanations as they offer a more intuitive way of requesting information. In general, while interactive XAI systems are often evaluated in terms of user satisfaction, there are limited studies that access user's objective model understanding. This is in particular the case for dialogue-based XAI approaches. In this paper, we close this gap by carrying out controlled experiments within a dialogue framework in which we measure understanding of users in three phases by asking them to simulate the predictions of the model they are learning about. By this, we can quantify the level of (improved) understanding w.r.t. how the model works, comparing the state prior, and after the interaction. We further analyze the data to reveal patterns of how the interaction between groups with high vs. low understanding gain differ. Overall, our work thus contributes to our understanding about the effectiveness of XAI approaches.

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