Privacy, Values and Machines: Predicting Opposition to Artificial Intelligence

Abstract

In this study we identify, for the first time, social determinants of opposition to artificial intelligence, based on the assessment of its benefits and risks. Using a national survey in Spain (n= 5,200) and linear regression models, we show that common explanations regarding opposition to artificial intelligence, such as competition and relative vulnerability theories, are not confirmed or have limited explanatory power. Stronger effects are shown by social values and general attitudes to science. Those expressing egalitarian values and privacy concerns, as well as those less predisposed to innovation in a general sense, are more prone to oppose both technological applications. Lastly, we found evidence that, as in other complex technological applications, a new cognitive shortcut is produced. In this case, we found a strong correlation (0.652, p <0.001) between public attitudes towards robotization in the workplace and towards artificial intelligence. We discuss the implications of this new cognitive schema, the "intelligent machine", as a new threatening or beneficial element.

The Mindsponge-based extensions of the Technology Acceptance Model: Behavioral determinants of the attitude toward of non-conscious emotional data harvesting

“…In other words, a stronger sense of digital self-efficacy over the new technologies leads to less worry over non-conscious data harvesting. This finding agrees with the literature on 'taming the algorithms,' where researchers found self-belief in the ability to exert one's agency in the computational platforms is indicative of active engagement with AI technologies (Lobera et al, 2020;Lu, 2020) Thus, our results demonstrate a solution for the privacy paradox or the personalization-privacy paradox, which is stated in the literature as although most people state strong preferences for the privacy of their personal data, they do not take steps to protect such data and often willing to give them up in the pursuit of personalized benefits (Ameen et al, 2022;Choi et al, 2019;Gerber et al, 2018). The solution is autonomy and control: when people perceive they have a higher sense of autonomy and mastery over the new technologies, they feel less worried about the collection and analysis of their non-conscious emotional data.…”

Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude

“…Policy-wise, we can improve EAI perception in patients through 1) explanation of AI's underlying algorithmic structure even only in high-level abstractions, 2) the current legal and ethical safeguards, 3) what role humans play in the decision-making process. Previous studies have converge into a common feature of human-machine relationship that when users perceive higher level of self-efficacy and having mechanisms to assert meaningful control over the algorithms, they become more comfortable with AI technologies (Lobera et al, 2020;Lu, 2020;McStay, 2020;Mohallick et al, 2018). Future studies can further identify the aspects of AI dependency that patients are averse to, whether it is AI replacement of human workers or AI dominant control in the treatment process.…”

Scientific Development of Educational Artificial Intelligence in Web of Science

“…Taking into account what has been indicated by various authors, we can find four types of artificial intelligence, among which are (a) recreational machines, which are purely reactive, without the capacity to form memories or to use their experiences to make decisions [7]; (b) limited memory, which can look at the past, allowing the analysis of data developed with anteriority [8]; (c) theory of mind, in which This allows the teacher to rectify and modulate the contents and the proposed tasks. Another benefit of implementing AI in the training of students is that they can benefit from supplementary tutoring by virtual assistants supported by AI [39].…”