Juan Rebanal
Papers
1
Total Citations
19
H-Index
1
About
Juan Rebanal is a researcher at the forefront of human-centered explainable AI (XAI), with a focus on bridging the gap between complex algorithmic systems and non-expert users. His work centers on designing interactive, intuitive explanations that demystify algorithms, moving beyond traditional black-box representations. Rebanal’s most cited paper, "XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering" (2021, 19 citations), introduces a novel approach that allows users to query an algorithm’s internal logic through natural language questions, making deterministic processes transparent and accessible. This contribution challenges conventional XAI methods by prioritizing user-driven exploration over static explanations. Rebanal’s research has significant implications for education, public policy, and everyday technology use, where algorithmic literacy is critical. His work is recognized for its innovative design methodology and user-centric perspective, earning him a growing reputation in the HCI and XAI communities. By empowering non-experts to understand and trust algorithms, Rebanal is shaping a future where technology is more accountable and inclusive.
Research Focus
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Top Papers
- 1