A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

Ha, Sunwoo; Monadjemi, Shayan; Garnett, Roman; Ottley, Alvitta

doi:10.1109/tvcg.2022.3209476

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…We believe recent advances in mixed‐initiative va systems fit this perspective. For example, user modeling techniques enable artificial agents to reason about analysts by observing their low‐level interactions [HMGO22] and recommendation systems take actions to assist analysts in exploration [SSKEA21, MHN * 22].…”

Section: Introductionmentioning

confidence: 99%

“…For example, some have focused on the complex cognitive processes humans exhibit while making decisions (e.g., [KKGMH21,WBFE17,LCO20, CWCO19, Ott20]), whereas others have focused on improving machine intelligence with the help of humans (e.g. [KHB21,CEH * 19, MHN * 22, HMGO22]). In this work, we focus on mixed‐initiative visual analytic settings where the system learns from the user's interactions and assists them in their analytic task.…”

Section: Introductionmentioning

confidence: 99%

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Human–Computer Collaboration for Visual Analytics: an Agent‐based Framework

Monadjemi

Guo

Gotz

et al. 2023

Computer Graphics Forum

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The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever‐expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent‐based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed‐initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human–Computer Collaboration for Visual Analytics: an Agent‐based Framework

Monadjemi

Guo

Gotz

et al. 2023

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

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Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

Cited by 2 publications

References 41 publications

Human–Computer Collaboration for Visual Analytics: an Agent‐based Framework

Human–Computer Collaboration for Visual Analytics: an Agent‐based Framework

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

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