Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model

Schwind, Valentin; Deierlein, Niklas; Poguntke, Romina; Henze, Niels

doi:10.1145/3290605.3300591

Cited by 19 publications

(15 citation statements)

References 34 publications

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“…The second measure of associations used the SCM in a partial replication of the study by Schwind et al (2019). Their study only looked at mobile devices and so the overlap with the present work related to only smartphones and tablets, and novelly looked at the associations made with desktops and laptops.…”

Section: Completed Workmentioning

confidence: 95%

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The Impact of Device Associations on Human Memory Performance

Steeds

2020

Electronic Workshops in Computing

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This project investigates whether associations (or stereotypes) connected to technological devices impact human memory, i.e do people remember more when using one device over another? To do this the project aims to investigate (1) the associations made with devices (e.g. utilitarian or hedonic qualities); (2) whether these associations impact human memory in controlled settings; and (3) if these effects transfer to applied settings. This paper describes the progress made so far in addressing Aim 1 and steps currently being undertaken to address Aim 2. The paper further identifies areas of interest surrounding Aim 3.

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Section: Completed Workmentioning

confidence: 95%

“…The SCM has been used in psychology to assess how a group is perceived along 2 axes, competence and warmth (Fiske et al, 2002). When applied to people using mobile devices, Schwind et al (2019) found that people using smartphones were seen as more contemptuous than those using other, similar devices (e.g. tablets).…”

Section: Related Workmentioning

confidence: 99%

The Impact of Device Associations on Human Memory Performance

Steeds

2020

Electronic Workshops in Computing

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“…There are also studies focusing on subgroups within social groups such as men and women (Eckes, 2002), immigrants (Lee and Fiske, 2006), lesbians (Brambilla et al, 2011), Native Americans (Burkley et al, 2017), social classes (Durante, Tablante and Fiske, 2017), and so on. In addition to social groups, SCM has been applied to other targets such as brands (Kervyn et al, 2012), social robotics (Mieczkowski et al, 2019;Oliveira et al, 2019), mobile devices (Schwind et al, 2019), mental health problems (Janda et al, 2019), tourist destinations (Shen et al, 2019), and even animals (Sevillano and Fiske, 2019).…”

Section: Extensions Of Scmmentioning

confidence: 99%

Stereotype contents, emotions, and public attitudes: How do Chinese people stereotype nations and national groups?

Liang

Jin

Zhou

et al. 2022

Chinese Journal of Sociology

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Previous studies have provided various explanations for people's attitudes toward foreign countries, but we still know very little about causal mechanisms of attitude formation. In this study, we propose that stereotypes play an important role in affecting individuals’ attitudes toward foreign countries. Drawing on survey data collected in 2019 and 2020 in China, we apply the stereotype content model to analyze Chinese people's attitudes toward five countries: the United States, Japan, India, Tanzania, and China itself. Our analyses show that: (a) Chinese respondents stereotype the five countries differently along two dimensions—warmth and competence—with the extremely high evaluations of China itself, indicating in-group favoritism; (b) warmth–competence combinations are closely linked with four emotions—admiration, envy, pity, and contempt—but are also affected by historical and cultural contexts; (c) stereotype contents can predict favorable attitudes toward foreign countries, with warmth stereotypes being more predictive than competence stereotypes; emotions can also predict favorability; and (d) stereotypes of countries are similar to stereotypes of persons from those countries, as is the case for emotions and attitudes.

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“…In general, bias can be partially attributed to stereotypes, which occur when one assigns traits to an individual based on preconceived notions about their group [17]. The Stereotype Content Model (SCM) is an established practical theory explaining stereotypes that has been applied in Human-Computer Interaction (HCI) (e.g., [53,40]). However, there is a lack of work applying the SCM to characterize biases introduced into datasets by crowdsourced labelers.…”

Section: Introductionmentioning

confidence: 99%

Investigating Labeler Bias in Face Annotation for Machine Learning

Haliburton¹,

Ghebremedhin²,

Welsch³

et al. 2023

Preprint

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In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key under-explored challenge is labeler bias, which can create inherently biased datasets for training and subsequently lead to inaccurate or unfair decisions in healthcare, employment, education, and law enforcement. Hence, we conducted a study to investigate and measure the existence of labeler bias using images of people from different ethnicities and sexes in a labeling task. Our results show that participants possess stereotypes that influence their decision-making process and that labeler demographics impact assigned labels. We also discuss how labeler bias influences datasets and, subsequently, the models trained on them. Overall, a high degree of transparency must be maintained throughout the entire artificial intelligence training process to identify and correct biases in the data as early as possible.

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Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model

Cited by 19 publications

References 34 publications

The Impact of Device Associations on Human Memory Performance

The Impact of Device Associations on Human Memory Performance

Stereotype contents, emotions, and public attitudes: How do Chinese people stereotype nations and national groups?

Investigating Labeler Bias in Face Annotation for Machine Learning

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