A Design Platform for Emotion-Aware User Interfaces

Lee, Eunjung; Kim, Gyu-Wan; Kim, Byung Soo; Kang, Mi-Ae

doi:10.1145/2668056.2668058

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Placebo-Control 4 [35,71,97,111] Placebo Study 12 [18,20,22,23,28,82,100,107,110,110] Acknowledge Placebo Effects as a Confound 11 [1,7,10,21,47,64,74,83,86,91,94,105] modalities. For example, one can adapt letter presentation in an E-reader to alpha oscillations; an indicator of workload in the electroencephalogram [55], reduce the number of interaction possibilities with the number of errors made by the user [45], or adapt the interface to the mood of the user [61]. Further examples include the user's context [90] and their interaction performance as well as their physiology [56] serving as implicit measurements for user interface adaptations.…”

Section: Category N Studiesmentioning

confidence: 99%

The Placebo Effect of Artificial Intelligence in Human-Computer Interaction

Kosch,

Welsch,

Chuang

et al. 2022

Preprint

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In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant's own task performance, sustained after interaction.These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.CCS Concepts: • Human-centered computing → User studies; HCI theory, concepts and models; Empirical studies in HCI .

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Section: Category N Studiesmentioning

confidence: 99%

The Placebo Effect of Artificial Intelligence in Human-Computer Interaction

Kosch,

Welsch,

Chuang

et al. 2022

Preprint

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“…Machines that can recognize emotions stand to play a significant role in the development of next-generation human-computer interaction systems [10,14,34,63]. Furthermore, with the emergence of social media, users are uploading millions of pictures everyday trying to express their emotions and thoughts with others.…”

Section: Introductionmentioning

confidence: 99%

Can the crowd tell how I feel? Trait empathy and ethnic background in a visual pain judgment task

Matsangidou

Otterbacher

Ang

et al. 2018

Univ Access Inf Soc

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Many advocate for artificial agents to be empathic. Crowdsourcing could help, by facilitating human-in-the-loop approaches and data set creation for visual emotion recognition algorithms. Although crowdsourcing has been employed successfully for a range of tasks, it is not clear how effective crowdsourcing is when the task involves subjective rating of emotions. We examined relationships between demographics, empathy, and ethnic identity in pain emotion recognition tasks. Amazon MTurkers viewed images of strangers in painful settings, and tagged subjects' emotions. They rated their level of pain arousal and confidence in their responses, and completed tests to gauge trait empathy and ethnic identity. We found that Caucasian participants were less confident than others, even when viewing other Caucasians in pain. Gender correlated to word choices for describing images, though not to pain arousal or confidence. The results underscore the need for verified information on crowdworkers, to harness diversity effectively for metadata generation tasks.

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