Summary of Xeq Scale For Evaluating Xai Experience Quality, by Anjana Wijekoon et al.
XEQ Scale for Evaluating XAI Experience Quality
by Anjana Wijekoon, Nirmalie Wiratunga, David Corsar, Kyle Martin, Ikechukwu Nkisi-Orji, Belen Díaz-Agudo, Derek Bridge
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to evaluating Explainable Artificial Intelligence (XAI) systems, focusing on creating a holistic and personalized experience for users. The authors develop the XAI Experience Quality (XEQ) Scale, which assesses the quality of XAI experiences across four dimensions: learning, utility, fulfillment, and engagement. The scale is validated through a large-scale pilot study, providing strong evidence for its effectiveness in evaluating user-centered XAI experiences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making artificial intelligence more transparent by creating better explanations. It’s important because people want to understand how AI decisions are made. Right now, it’s hard to measure if an AI explanation is good or not. The authors created a new way to do this, called the XAI Experience Quality (XEQ) Scale. They tested it with many people and found that it works well. |
