Improving Artificial Intelligence Through User Feedback in eXplainable Artificial Intelligence (XAI) Systems - Design Recommendations for Sustaining Long-Term Human- XAI Interaction in Industry 4.0

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dc.contributor.authorHashmati, Negin
dc.contributor.authorWärnberg, Hugo
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineeringen
dc.contributor.examinerDahlstedt, Palle
dc.contributor.supervisorObaid, Mohammad
dc.date.accessioned2024-09-20T07:20:53Z
dc.date.available2024-09-20T07:20:53Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractThis thesis explores Human-XAI interaction and the vital role of user motivation in providing feedback to XAI systems in industrial settings, focusing on the design of Incremental Explanatory Training (IET) components that enable feedback. A welladjusted XAI interface is necessary for feedback provision and in turn, the feedback is used to improve the accuracy of the Artificial Intelligence. Drawing from the interdisciplinary field of XAI, the industry 4.0 context, and User-Centered Design (UCD) practices, it investigates the motivations required for sustained user engagement, identifies interface components that enhance feedback provision, and offers design recommendations for optimizing the user experience and sustaining motivation for feedback provision. This research addresses three key questions: 1) the types of motivations necessary for user feedback, 2) the interface components fostering motivation, and 3) design recommendations for effective feedback mechanisms adhering to motivation. As a result of this research, it was shown that both extrinsic and intrinsic motivations are necessary to encourage sustained feedback provision and that interface components with XAI improvement statistics and effective interaction modalities are vital for fostering motivation. Eight design recommendations were identified that offer generalized insights into important considerations for IET components to contribute to sustained motivation and user engagement for feedback provision.
dc.identifier.coursecodeDATX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308735
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectmotivation
dc.subjectfeedback
dc.subjectexplainable artificial intelligence
dc.subjectindustry 4.0
dc.subjectdesign recommendations
dc.subjecthuman-AI interaction
dc.subjectinteraction design
dc.subjectuser-centered design
dc.subjectincremental explanatory training
dc.titleImproving Artificial Intelligence Through User Feedback in eXplainable Artificial Intelligence (XAI) Systems - Design Recommendations for Sustaining Long-Term Human- XAI Interaction in Industry 4.0
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeInteraction design and technologies (MPIDE), MSc
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