Surveillance, security, and AI as technological acceptance

Park, Yong Jin; Jones-Jang, S. Mo

doi:10.1007/s00146-021-01331-9

Cited by 24 publications

(12 citation statements)

References 32 publications

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“…A large body of literature investigating AI related risks already exists; showing that perceived risks usually center around the misuse of AI by government or tech companies, loss of privacy, and surveillance (Barth & de Jong, 2017;Park & Jones-Jang, 2022;Zhang & Dafoe, 2019;Zhang & Dafoe, 2020). Kelley et al (2021), for example, conducted a multi-national survey, in Australia, Canada, USA, South Korea, France, Brazil, India, and Nigeria, corroborating results from previous studies by showing that all countries were expressing concern over job loss and privacy.…”

Section: Risks and Opportunities Of Ai Applicationssupporting

confidence: 73%

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

Said¹,

Potinteu²,

Brich³

et al. 2022

Preprint

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Artificial intelligence (AI) applications are increasingly used in everyday life. Whereas some of them are widely accepted (e.g., automatically compiled playlists), others are highly controversial (e.g., use of AI in the classroom). While public discourse is dominated by perceptions of the risks associated with AI, we take a fundamentally different approach of measuring the perceived risks and opportunities of AI applications considering people's knowledge and confidence in their own knowledge. To this end, we assessed in two studies (N = 394 and N = 437) how knowledge about AI as well confidence in AI knowledge is related to participants risk-opportunity perception of AI scenarios from three domains: media, medicine, and autonomous driving. Results showed that both AI knowledge and confidence in AI knowledge are important predictors regarding people’s risk-opportunity perception beyond people's attitudes towards AI. More specifically, people with more knowledge about AI exhibited a so-called risk blindness in that they were underestimating the risks. On the other hand, higher confidence in ones’ AI knowledge impacted participants opportunity perception. Knowledge and confidence thus open a new dimension of understanding people’s perception of risks and opportunities in AI.

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Section: Risks and Opportunities Of Ai Applicationssupporting

confidence: 73%

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

Said¹,

Potinteu²,

Brich³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We are not arguing that providing access to devices or training is unimportant, but that digital inequality and its interrelatedness with social inequalities along lines of class, age, gender, and ethnicity demand that policy, practices, and research address digital inequality in all its complexity. As is already happing in the field of Artificial Intelligence acceptance, see for example the study of Lutz (2019) and Park and Jones‐Jang (2022).…”

Section: Discussionmentioning

confidence: 99%

“Never good enough.” A situated understanding of the impact of digitalization on citizens living in a low socioeconomic position

2022

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Digital inequality is a critical issue in our knowledge economy. Often digital inequality is framed as a problem that only older people encounter. This rather stereotypical portrayal can result in improvised and unsuccessful policy actions. The goal of the article is to take the experiences of citizens who never or seldom use information and communication technology as a point of departure to show the impact and outcomes of digitalization on the lives of people with a low socioeconomic position (LSEP) and across different intersections, such as gender, poverty, ethnicity, and age. The results of this qualitative study emphasize that not being able to use the Internet negatively impacts feelings of belonging and self-confidence. Citizens need to invest continually in new or different equipment and in skills to keep up. Opportunities to invest are shaped at the nexus of gender, class, educational level, and occupational status. Therefore, simply training people in particular digital skills is insufficient. We show that the complexity of digital inequality and its interrelatedness with social inequalities along lines of class, age, gender, and ethnicity demand that policy and practices address digital inequality across the entire scope.

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“…In this vein, we do not have immediate answer, with our dataset, as to precisely how societal mechanism might function to reverse cultivation against misinformation. But we suspect that the general trust that is related to having close community attachment of societal ties, as well as personal-level connections, might be in potential mix, especially when interactive settings are egalitarian and thus afford opportunities to filter in and out diverse interpretations (Lee et al, 2018; Park and Chung, 2017; Park and Jones-Jang, 2022; Uslaner, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…It is worth noting that we are not dismissive about a suggestion that functional features like crosschecking sources can be an effective corrective tool. Yet we are concerned that social media platforms with their institutional gatekeeping power exert exclusive controls over public information at their whim, setting the condition under which misinformation in its sharing and distribution remains “sticky.” As we documented in this work, misinformation, once it sets in motion, will be hard to reverse its influence without persistent institutional effort—for example, to improve algorithm designs that mitigate misinformed traffic (Tandoc et al, 2020; Park and Jones-Jang, 2022). Our finding that misinformation exposure was significantly related to Covid-19 news-following makes it also hard to be optimistic about good will of media institutions, whether increased access to news-information takes a form of social or traditional media consumption.…”

Section: Conclusion and Implicationmentioning

confidence: 94%

Social media, misinformation, and cultivation of informational mistrust: Cultivating Covid-19 mistrust

2022

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We adopt the cultivation theory to identify the ways the increased exposures to (mis)information in social media and traditional media cultivate the perceptions of (1) informational mistrust and (2) ill-confidence in dealing with Covid-19 pandemic risk. Importantly, we expanded the theory and hypothesized about the roles of informal societal ties by investigating whether local community attachment and frequent friend-family interactions can mitigate the formation and consequence of mistrust arising from the exposure to misinformation. We found that the higher exposure to Covid-19 misinformation, as promulgated in both active and passive social media uses, was related to informational mistrust that was also linked to a lower level of confidence in telling the veracity of misinformation. Findings also show the significant relationships between two forms of informal social ties and misinformation confidence. We discuss how the abundance of information sources fuels misinformed citizenry as individuals are left alone to navigate increasingly confusing influx of unfounded misinformation abounded in social media.

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Surveillance, security, and AI as technological acceptance

Cited by 24 publications

References 32 publications

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

“Never good enough.” A situated understanding of the impact of digitalization on citizens living in a low socioeconomic position

Social media, misinformation, and cultivation of informational mistrust: Cultivating Covid-19 mistrust

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