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Paper Number
1936
Paper Type
Completed
Description
Explainability considered a critical component of trustworthy artificial intelligence (AI) systems, has been proposed to address AI systems’ lack of transparency by revealing the reasons behind their decisions to lay users. However, most explainability methods developed so far provide static explanations that limit the information conveyed to lay users resulting in an insufficient understanding of how AI systems make decisions. To address this challenge and support the efforts to improve the transparency of AI systems, we conducted a design science research project to design an interactive explainable artificial intelligence (XAI) system to help lay users understand AI systems’ decisions. We relied on existing knowledge in the XAI literature to propose design principles and instantiate them in an initial prototype. We then conducted an evaluation of the prototype and interviews with lay users. Our research contributes design knowledge for interactive XAI systems and provides practical guidelines for practitioners.
Recommended Citation
Meza Martínez, Miguel Angel and Mädche, Alexander, "Designing Interactive Explainable AI Systems for Lay Users" (2023). ICIS 2023 Proceedings. 5.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/5
Designing Interactive Explainable AI Systems for Lay Users
Explainability considered a critical component of trustworthy artificial intelligence (AI) systems, has been proposed to address AI systems’ lack of transparency by revealing the reasons behind their decisions to lay users. However, most explainability methods developed so far provide static explanations that limit the information conveyed to lay users resulting in an insufficient understanding of how AI systems make decisions. To address this challenge and support the efforts to improve the transparency of AI systems, we conducted a design science research project to design an interactive explainable artificial intelligence (XAI) system to help lay users understand AI systems’ decisions. We relied on existing knowledge in the XAI literature to propose design principles and instantiate them in an initial prototype. We then conducted an evaluation of the prototype and interviews with lay users. Our research contributes design knowledge for interactive XAI systems and provides practical guidelines for practitioners.
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