This study explores the phenomenon of artificial intelligence (AI) aversion within the context of public policy, building on prior research on algorithmic aversion. I aim to establish a clear conceptual distinction between algorithms and AI in the public’s perception and develop a robust metric for assessing AI aversion. Utilizing a national survey, I employed affective imagery testing to compare Americans emotional responses towards AI, algorithms, and advanced technology. The findings reveal that AI elicits significantly more negative emotional responses than the (...) other two, indicating its unique position in public perception. I then construct the Artificial Intelligence Aversion Index (AIAI) based on responses to policy-related vignettes. Regression analyses showed a strong negative relationship between the AIAI and public support for both current and future AI applications within public policy, with aversion more pronounced towards potential future uses. These insights underscore the importance of understanding public sentiment towards AI to inform policymaking as well as helping to establish a framework by which to evaluate aversion levels. (shrink)

AI systems, which receive vast amounts of information including privacy information, are emerging. Protecting the privacy of the general public is an important issue for democracies. In this study, “Public space watched by AI- connected cameras” is taken as an example of an AI-system that is expected to be used for public purposes and has a relatively high privacy violation risk. It is defined as a wide public area where every point is monitored by multiple AI-connected street cameras. The following (...) four socially acceptable applications of “Public space watched by AI- connected cameras” are discussed: (1) Human/vehicle tracking, (2) Watching over children, (3) Detection of abnormal situations, (4) Criminal and terrorist behavior pattern analysis. As for privacy protection measures for AI systems, in addition to reliable protection itself, reliable recording and auditing to deter misuse and abuse is considered a prerequisite to ensure that the general public can feel secure. In this paper, the concept of “Verifiable record of AI output”, which is a verifiable and tamper-proof record and disclosure of AI output is proposed. “Verifiable record of AI output” is based on the idea that it may be more realistic to apply a simple exit control only for AI output to confirm rule conformance, rather than anonymizing huge AI inputs such as Big Data or controlling internal AI processes that are black-boxed. A concrete example of implementing “Verifiable record of AI output” in “Public space watched by AI-connected cameras” is proposed. In the “Verifiable record of AI output”, the AI system is enclosed in an outbound firewall, and only AI outputs that are confirmed to be in line with “the purpose of use approved in advance through social discussion” are allowed to be transmitted. The process from application to permission of transmission is anonymized and disclosed in a verifiable and tamper-proof manner. This public record can be verified by the general public or an auditing organization to ensure that any misuse or abuse of the AI system will be exposed. The “Verifiable record of AI output” proposed in this paper is characterized by reliable measurement and recording of AI output, and its disclosure for auditing purposes. The possibility of using this as a template to apply to other AI systems that receive Big Data input, including the privacy of the general public, is considered. (shrink)