Can the Crowd Tell How I Feel? Trait Empathy and Ethnic Background in a Visual Pain Judgment Task

Matsangidou, Maria and Otterbacher, Jahna and Ang, Chee Siang and Zaphiris, Panayiotis (2018) Can the Crowd Tell How I Feel? Trait Empathy and Ethnic Background in a Visual Pain Judgment Task. Universal Access in the Information Society, . ISSN 1615-5289. E-ISSN 1615-5297. (doi:https://doi.org/10.1007/s10209-018-0611-y) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

ABSTRACT Many advocate for artificial agents to be empathic. Crowdsourc- ing could help, by facilitating human-in-the-loop approaches and dataset crea- tion 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 ex- amined 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 cor- related 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.

Item Type: Article
Uncontrolled keywords: Crowdsourcing · Ethnicity · Pain · Distress · Empathy · Image metadata
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Digital Media
Depositing User: Jim Ang
Date Deposited: 23 Jan 2018 09:14 UTC
Last Modified: 08 May 2018 10:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65767 (The current URI for this page, for reference purposes)
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