Jordan Combitsis
Papers
1
Total Citations
19
H-Index
1
About
Dr. Jordan Combitsis is a leading researcher in human-centered explainable AI (XAI), specializing in bridging the gap between complex algorithmic systems and non-expert users. Their most-cited work, "XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering" (2021, 19 citations), introduces a groundbreaking interactive approach that allows users to query deterministic algorithms through natural language question-answering, transforming opaque "black boxes" into transparent, understandable processes. This contribution directly addresses a critical challenge in XAI: moving beyond static, expert-oriented explanations to dynamic, user-driven understanding. By designing and evaluating a probe that empowers non-experts to explore algorithmic internal states, Combitsis has laid foundational groundwork for more accessible and equitable AI systems. Their research has significant implications for education, public policy, and any domain where stakeholders must trust and verify algorithmic decisions. Dr. Combitsis’s work is notable for its rigorous human-centered design methodology, blending cognitive science principles with technical system building to create truly usable explanations. As the demand for algorithmic accountability grows, their contributions are increasingly vital, shaping how we design AI that is not only powerful but also comprehensible to the people it affects.
Research Focus
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Top Papers
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